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PsyPost – Psychology News Daily Digest (Unofficial)

 

(https://www.psypost.org/moderate-coffee-consumption-during-pregnancy-unlikely-to-cause-adhd-in-children/) Moderate coffee consumption during pregnancy unlikely to cause ADHD in children
Nov 3rd 2024, 08:00

(https://www.who.int/tools/elena/interventions/caffeine-pregnancy#:~:text=WHO%20Recommendations,and%20low%20birth%20weight%20neonates) International guidelines recommend people limit how much coffee they drink during pregnancy. Consuming caffeine – a stimulant – while pregnant (https://doi.org/10.17352/2455-5479.000148) has been linked to how the baby’s brain develops.
Some (https://pubmed.ncbi.nlm.nih.gov/2662862/) studies have shown increased coffee consumption during pregnancy is associated with the child having neurodevelopmental difficulties. These may include traits linked to attention-deficit hyperactivity disorder (ADHD), such as difficulties with language, motor skills, attention, hyperactivity and impulsive behaviour.
But is coffee the cause? (https://www.cambridge.org/core/journals/psychological-medicine/article/mendelian-randomization-analysis-of-maternal-coffee-consumption-during-pregnancy-on-offspring-neurodevelopmental-difficulties-in-the-norwegian-mother-father-and-child-cohort-study-moba/14D89D571E177847FF8A55F57B5AE7D8) Our new research aimed to clear up the sometimes confusing advice about drinking coffee during pregnancy.
We studied tens of thousands of pregnant women over two decades. The results showed – when other factors like genes and income were accounted for – no causal link between drinking coffee during pregnancy and a child’s neurodevelopmental difficulties. That means it’s safe to keep drinking your daily latte according to (https://www.health.gov.au/resources/publications/nutrition-advice-during-pregnancy?language=en) current recommendations.
What we were trying to find out
Past research has (https://pubmed.ncbi.nlm.nih.gov/2662862/) identified a link between drinking coffee during pregnancy and a child’s neurodevelopmental difficulties. But it hasn’t been able to establish caffeine as the direct cause.
Biological changes during pregnancy (https://pubmed.ncbi.nlm.nih.gov/7302604/) reduce caffeine metabolism. This means the caffeine molecules and metabolites (the molecules produced while breaking down the caffeine) take longer to be cleared from the body.
Additionally, past studies have shown caffeine and its by-products can (https://doi.org/10.1016/0006-2952(62)90106-5) cross the placenta. The fetus (https://pubmed.ncbi.nlm.nih.gov/7302604/) does not have the necessary enzymes to clear them, and so it was thought that caffeine metabolites may impact the developing baby.
However it can be hard to identify whether coffee directly causes changes to the fetus’s brain development. Pregnant women who drink coffee may differ from those who don’t in a number of other ways. And it could be these variables – not coffee – that affect neurodevelopment.
These variables, known as “confounding factors” might include how much people drink or smoke while pregnant, or a parent’s income and education. For example, we know people who tend to drink coffee also tend to (https://www.sciencedirect.com/science/article/abs/pii/S0195666305001297?via%3Dihub) drink more alcohol and (https://pubmed.ncbi.nlm.nih.gov/26750569/) smoke more cigarettes than (https://pubmed.ncbi.nlm.nih.gov/16298019/) those who don’t drink coffee.
Our study aimed to look at the effect of drinking coffee on neurodevelopmental difficulties, isolated from these confounding factors.
What we did
We know genes play a role in (https://www.nature.com/articles/mp2014107) how many cups of coffee a person consumes per day. Our study used (https://www.ncbi.nlm.nih.gov/books/NBK62433/) genetics to compare the development of children whose mothers did and did not carry genes linked to increased coffee consumption.
The study looked at tens of thousands of families registered in (https://doi.org/10.1093/ije/dyw029) the Norwegian Mother, Father and Child Cohort Study. All pregnant women in Norway between 1999 and 2008 were invited to participate and 58,694 women took part with their child.
Parents reported how much coffee they drank before and during pregnancy. Mothers also completed questionnaires about their child’s neurodevelopmental traits between six months and eight years of age.
The questions covered many traits, including difficulties with attention, communication, behavioural flexibility, regulation of activity and impulses, as well as motor and language skills.
The parents and children also provided genetic samples. This allowed us to control for genetic variants shared between mother and child and isolate the behaviour of coffee drinking.
What we found
We were able to look at causality through this method of adjusting for potential confounding factors in the environment (the mother smoking or drinking alcohol, the parents’ education and income).
The results showed no strong causal link between increased maternal coffee consumption and children’s neurodevelopmental difficulties.
The difference in findings between our and previous studies may be explained by our work separating the effect of coffee from the effect of other variables, as well as genetic predisposition to neurodevelopmental conditions.
Our study has limitations. Importantly, we were only able to rule out strong effects of coffee on neurodevelopmental difficulties, and it is possible small effects may exist.
We only investigated offspring neurodevelopmental traits, and coffee consumption during pregnancy could impact the mother or child in other ways.
However (https://academic.oup.com/ije/article/52/1/165/6605011?login=false) we have previously shown coffee consumption during pregnancy did not have strong causal effects on birth weight, gestational duration, risk of miscarriage or stillbirth. But other outcomes – such as mental health or a child’s risk for heart disease and stroke later in life – should be investigated.
Overall, our study supports (https://www.who.int/tools/elena/interventions/caffeine-pregnancy) current clinical guidelines that state low to moderate consumption of coffee during pregnancy is safe for the mother and developing baby.
For most people, that means sticking below (https://www.pregnancybirthbaby.org.au/caffeine-during-pregnancy#safe) 200mg of caffeine per day – usually equivalent to one espresso or two instant coffees – should be safe. If you have concerns, it’s best to speak to your clinician.
 
This article is republished from (https://theconversation.com) The Conversation under a Creative Commons license. Read the (https://theconversation.com/does-drinking-coffee-while-pregnant-cause-adhd-our-study-shows-theres-no-strong-link-241015) original article.

(https://www.psypost.org/early-life-stress-predicts-negative-emotionality-and-inflammation-in-alcohol-use-disorder/) Early life stress predicts negative emotionality and inflammation in alcohol use disorder
Nov 3rd 2024, 06:00

A recent study conducted by researchers at UCLA and published in the journal (https://www.nature.com/articles/s41386-024-01877-4) Neuropsychopharmacology has revealed that individuals with alcohol use disorder who experienced significant stress or trauma during childhood often face more intense emotional difficulties and elevated inflammation as adults.
Those with a history of early life stress showed higher levels of negative emotions and a marked increase in inflammatory markers compared to others with the same disorder but without early traumatic experiences. These findings suggest that childhood stress might lead to unique biological and emotional changes in people with alcohol use disorder, which could have implications for targeted treatment strategies.
Previous studies have shown that early life stress can increase the risk of various psychiatric conditions, including alcohol use disorder. However, little research has been conducted on whether people with alcohol use disorder who experienced early life stress have distinct emotional and biological profiles compared to those who did not.
“Early life stress—such as experiencing abuse and neglect or growing up in a difficult household environment—is highly prevalent and has been shown to be one of the most powerful predictors of adverse mental health outcomes, including alcohol use disorder,” said study author Dylan E. Kirsch, a postdoctoral fellow in the Department of Psychology at UCLA.
“Early life stress results in long-lasting changes in the brain and body, which increase the risk for these adverse mental health outcomes. People with a history of early life stress tend to face a more severe mental illness course and have a worse treatment response compared to those with the same diagnosis but no early life stress history. Research also shows that individuals with a history of early life stress show unique biological alterations that differentiate them from others with the same diagnosis but no early life stress history.”
“I was struck by this research, which converges to suggest that people with the same primary mental health diagnosis may differ in their clinical and biological profiles depending on their early life stress history. This research, however, has largely been conducted in individuals with mood disorders.”
“Therefore, I wanted to know whether these early life stress-related differences are also observed in individuals with alcohol use disorder,” Kirsch explained. “Specifically, I was interested in asking the question: Do individuals with alcohol use disorder and a history of early life stress differ in their clinical presentation and underlying biology from those with alcohol use disorder but no early life stress history?”
The study included 163 adults with alcohol use disorder who were seeking treatment. Each participant was evaluated for childhood trauma and stress using the Adverse Childhood Experiences questionnaire, a tool that measures different types of early life stress, including abuse, neglect, and family dysfunction. Based on their responses, participants were grouped into three categories: those with no history of early life stress, those with moderate levels of stress (one to three instances of adverse childhood experiences), and those with high levels of stress (four or more adverse experiences).
To understand the emotional and biological impacts, the researchers focused on two areas associated with addiction: negative emotionality and incentive salience. Negative emotionality reflects feelings of sadness, anxiety, and other negative emotions, while incentive salience relates to the desire or urge for alcohol. They used psychological assessments to measure these aspects, specifically examining how much participants thought about drinking and their feelings of guilt or sadness after drinking.
In addition, 98 of the participants provided blood samples, which were tested for a specific marker of inflammation known as C-reactive protein. Elevated levels of this protein can indicate increased inflammation, which has been associated with health issues like cardiovascular disease and is believed to be linked to long-term effects of early life stress.
The researchers found clear differences between individuals with high levels of early life stress and those with little or no such stress. Participants with a high history of early life stress reported higher levels of negative emotionality, including feelings of sadness, guilt, and anxiety, compared to the other two groups.
The findings also indicated a link between early life stress and inflammation. Those in the high-stress group showed significantly higher levels of C-reactive protein in their blood, indicating a higher level of inflammation. This was not observed in the moderate or no-stress groups, suggesting that more severe childhood stress might be required to trigger an inflammatory response later in life. The researchers observed that both age and body mass index played a role in inflammation levels but found that early life stress remained a key factor even when controlling for these variables.
“Individuals with an alcohol use disorder may exhibit unique clinical and biological characteristics based on their early life stress history,” Kirsch told PsyPost. “Specifically, our findings suggest that those with alcohol use disorder and a history of early life stress experience heightened negative emotionality and have higher levels of peripheral inflammation compared to those without early life stress.
“As the field of psychiatry moves towards precision medicine approaches, understanding these differences in alcohol use disorder presentation and their biological underpinnings is essential. This insight can guide the development of targeted treatment tailored to the unique presentation of individuals with a history of early life stress.”
Interestingly, early life stress did not appear to impact incentive salience, meaning that childhood trauma did not make individuals more inclined to think about or crave alcohol.
“We had hypothesized that early life stress would also be associated with greater incentive salience—or motivated ‘wanting’ of alcohol,” Kirsch told PsyPost. “Contrary to this prediction, early life stress was not associated with incentive salience in our sample, which suggests a dissociation of the effects of early life stress on alcohol use disorder clinical phenomenology.”
One limitation is that the study was cross-sectional, meaning it only provides a snapshot of participants’ experiences at one point in time. As a result, it cannot establish a causal relationship between early life stress and later inflammation or emotional challenges. Future research could address this by following individuals over time to understand how early life stress impacts the development of alcohol use disorder and related symptoms.
“Additionally, there is growing evidence that different forms of early life stress are associated with unique mental health outcomes,” Kirsch noted. “Our study did not have a large enough sample to investigate different types of early life stress. Therefore, we need to conduct large-scale longitudinal studies to further explore this topic.”
The study highlights the potential need for tailored treatment approaches for people with alcohol use disorder who have experienced early life stress. For instance, focusing on managing negative emotions and addressing inflammation could be particularly beneficial for these individuals. Additionally, anti-inflammatory treatments, which have shown promise in reducing symptoms in people with mood disorders and a history of early life stress, might be explored as an option for people with alcohol use disorder.
“I would love to extend this line of research in the following ways: (1) Understand how a history of early life stress influences in other biobehavioral processes in alcohol use disorder—such as brain structure and function, subjective response to alcohol, alcohol cue-reactivity, etc.; and (2) Understand how a history of early life stress influences treatment response in individuals with alcohol use disorder,” Kirsch said.
The researchers have also found evidence that biological sex may play a crucial role in moderating the inflammatory response associated with early life stress.
“While we did not examine this in the present study, existing research suggests that sex at birth may be an important variable moderating biological response to early life stress,” Kirsch explained. “We recently conducted another study to ask the question: Does biological sex moderate the effects of early life stress on peripheral inflammation in alcohol use disorder? (see: (https://doi.org/10.1016/j.drugalcdep.2024.112474) https://doi.org/10.1016/j.drugalcdep.2024.112474).”
“Our results suggest that biological sex moderates the effects of early life stress on peripheral inflammation in adults with alcohol use disorder. Specifically, findings suggest females with alcohol use disorder may be more vulnerable to the early life stress-related adaptations to the immune system, potentially resulting in a proinflammatory state in adulthood.”
The study, “(https://doi.org/10.1038/s41386-024-01877-4) Early life stress is associated with greater negative emotionality and peripheral inflammation in alcohol use disorder,” was authored by Dylan E. Kirsch, Erica N. Grodin, Steven J. Nieto, Annabel Kady, and Lara A. Ray.

(https://www.psypost.org/voters-more-trusting-of-elections-when-polls-are-supervised-by-multiple-groups/) Voters more trusting of elections when polls are supervised by multiple groups
Nov 2nd 2024, 18:00

A recent study has shown that voters are more likely to believe vote counts are accurate when election results are monitored by a range of different officials, including government election workers, political party representatives, and non-partisan observers. The research suggests that having various monitoring groups present at polling places can increase voter confidence in the election process, regardless of voters’ political leanings or pre-existing trust in electoral bodies.
The study, published in the (https://www.cambridge.org/core/journals/journal-of-experimental-political-science/article/when-do-voters-see-fraud-evaluating-the-effects-of-poll-supervision-on-perceptions-of-integrity/CDFFB8C264F2D6553671B48A978E35D2) Journal of Experimental Political Science, was conducted by Fanisi Mbozi from New York University Abu Dhabi.
Mbozi’s work builds on prior research that has largely focused on the role of non-partisan observers in enhancing perceptions of election integrity. However, Mbozi’s research expands this scope to examine how political party agents and government officials also contribute to voter confidence in the reliability of vote counts. By investigating these additional influences, the study sheds light on how diverse polling supervision might counter public distrust in election processes, especially in regions where vote-counting disputes are common.
The motivation behind this research stems from recent election controversies in countries like Malawi and Kenya, where vote-count disputes have caused significant public unrest. In these settings, voters often have little direct knowledge of what happens in the vote-counting process, relying instead on limited information provided by groups allowed to observe vote counting.
Political party agents and government officials are frequently among these monitors, working alongside non-partisan election observers to oversee the fairness of the process. Mbozi sought to understand whether these different groups could independently affect voters’ perceptions of election integrity, given that each group’s presence may signify different levels of oversight and protection against fraud. In regions where past irregularities in the vote-counting process have led to disputes, the study aimed to provide insights into which aspects of poll supervision might foster greater confidence in election results.
Mbozi conducted the study through a conjoint experiment, which involved 390 respondents from Malawi and Kenya. Conjoint experiments present participants with a set of options that vary systematically along several dimensions; in this case, the experiment featured vote-tally sheets showing different combinations of monitors’ signatures to simulate polling station conditions.
Each tally sheet image either included or omitted signatures from government election officials, political party agents, and non-partisan observers, creating a visual prompt for voters to evaluate. Participants were asked to choose which tally sheet, in their view, presented more reliable vote counts, with each participant viewing and evaluating multiple pairs of tally sheets. The absence of a group’s signature implied that group’s absence from monitoring the vote count.
By observing which tally sheets participants deemed more trustworthy, Mbozi was able to gauge the perceived importance of each monitor type. Additionally, the study asked participants follow-up questions about their selections to understand the specific reasons for their choices. This allowed the researcher to explore whether voter preferences for certain monitors were based on partisan affiliation, trust in electoral institutions, or prior awareness of the observer groups’ roles.
Overall, the experimental design sought to capture voters’ initial reactions to monitored and unmonitored polling conditions, offering insights into the qualities that might influence public confidence in election outcomes.
The findings showed that the presence of any one of these groups—government election officials, party agents, or non-partisan observers—positively impacted voter perceptions of election integrity. Interestingly, voters showed the highest level of trust when multiple groups were represented, with the presence of both political party agents (especially from opposing parties) and a non-partisan observer creating the greatest sense of reliability. This suggests that a diversity of monitors may serve as a strong deterrent to perceptions of fraud, as voters likely interpret the involvement of multiple perspectives as added accountability.
Furthermore, voters appeared to value the presence of non-partisan observers, even if they had limited knowledge of these groups’ roles beforehand. The study’s results also indicated that voters’ responses did not depend significantly on their prior trust in the country’s electoral institutions or their party affiliation. This suggests that the benefits of poll monitoring extend beyond individual political identities or institutional loyalty. The outcomes may reflect a more general preference for transparency and diverse oversight, regardless of voters’ personal backgrounds or political leanings.
While the study’s results provide valuable insights, there are some limitations. Since the research was conducted online, the sample population was more likely to be educated and politically aware than the average voter in Malawi or Kenya, which could affect how representative the findings are for broader populations, particularly in more rural or less digitally connected areas. Future research might explore the impact of poll monitoring in these different demographic segments to understand how perceptions vary across educational and political engagement levels.
The study, “(https://www.cambridge.org/core/journals/journal-of-experimental-political-science/article/when-do-voters-see-fraud-evaluating-the-effects-of-poll-supervision-on-perceptions-of-integrity/CDFFB8C264F2D6553671B48A978E35D2) When Do Voters See Fraud? Evaluating the Effects of Poll Supervision on Perceptions of Integrity,” was published July 30, 2024.

(https://www.psypost.org/majority-of-americans-support-supervised-use-of-psilocybin-for-mental-health-and-well-being/) Majority of Americans support supervised use of psilocybin for mental health and well-being
Nov 2nd 2024, 16:00

A recent study published in (https://www.tandfonline.com/doi/full/10.1080/21507740.2024.2303154) AJOB Neuroscience found that a majority of Americans support psilocybin, a psychedelic compound from certain mushrooms, for supervised medical treatment and well-being enhancement. This strong bipartisan approval highlights public openness to legalized and controlled use of the drug for both medical and personal enhancement purposes, though with caution for future policy.
Psilocybin is a naturally occurring psychedelic compound found in certain types of mushrooms, often referred to as “magic mushrooms.” When ingested, psilocybin interacts with serotonin receptors in the brain, creating profound changes in perception, mood, and thought processes. It has long been classified as a Schedule 1 substance in the United States, meaning it has been regarded as having a high potential for abuse and no accepted medical use.
However, recent research has sparked new interest in psilocybin due to its potential benefits, particularly in mental health. Studies have shown that when used under controlled, therapeutic conditions, psilocybin may (https://www.psypost.org/psilocybin-may-offer-fast-and-lasting-antidepressant-benefits-by-enhancing-brain-plasticity/) help reduce symptoms in individuals suffering from conditions such as treatment-resistant depression. Unlike many medications, psilocybin is thought to be non-addictive and (https://www.psypost.org/the-science-of-magic-mushrooms-fascinating-findings-from-7-new-studies-of-psilocybin/) may even improve well-being in healthy individuals.
The researchers conducted this study to address a lack of data on public attitudes toward psilocybin in light of recent legalization steps, such as in Oregon, where psilocybin is now allowed in licensed settings for both mental health treatment and well-being enhancement. The study aimed to gauge moral opinions on the supervised use of psilocybin as well as attitudes towards its use beyond clinical needs to enhance general well-being, an area where previous studies were lacking.
To conduct the study, the researchers recruited a nationally representative sample of 805 Americans through an online platform. This sample was then refined to 795 participants after excluding those who did not meet attention criteria. The participants’ ages ranged from 18 to 92, with a median age of 44. The sample was diverse, encompassing various genders, races, and political affiliations, making it reflective of the United States’ demographics.
Each participant received background information about psilocybin, including its origin, effects, and legal status. They were also informed about the new Oregon law permitting supervised psilocybin use and asked to imagine a similar law on a national level.
Participants were then randomly assigned to read one of two scenarios where psilocybin was used under professional supervision in a licensed setting. In one scenario, the drug was used to treat a mental health condition, while in the other, it was used by a healthy individual seeking well-being enhancement.
After reading their assigned scenario, participants were asked to assess the moral acceptability of the individual’s decision to use psilocybin in that controlled environment. Participants also completed several moral and demographic assessments, covering their values, empathy, and political views, among other factors.
The study revealed that 89% of participants viewed supervised psilocybin use for mental health treatment as morally acceptable, and 85% approved of its use for well-being enhancement. Although slightly more participants supported medical use than enhancement, the general trend showed broad acceptance. Interestingly, this approval spanned across political lines, with both liberals and conservatives showing a strong endorsement for supervised psilocybin use in licensed settings.
However, support varied slightly with age and political leanings: younger adults and political liberals expressed higher approval than older adults and conservatives. Furthermore, participants who prioritized values like care and empathy were more inclined to view psilocybin use positively, linking these values with their desire for both patient and individual well-being.
Despite the study’s insightful results, there are some limitations. First, it focused solely on supervised use in licensed settings, omitting public opinions on unsupervised or recreational uses of psilocybin, which may carry more risks. Additionally, the study did not examine whether participants’ knowledge or beliefs about psilocybin might change over time with continued media and scientific attention on psychedelics.
Future research might look into attitudes toward unsupervised use or expand on how people perceive risks associated with psychedelics in different contexts. Understanding these attitudes could guide lawmakers in developing policies that align with public sentiment while addressing the risks associated with non-supervised psychedelic use.
“Caution is also required in relation to the apparent hype bubble now surrounding the so-called ‘psychedelic Renaissance,'” the researchers concluded. “Given the early stage of the field, both over- and understatements of trial results are not uncommon. Current scientific evidence, however, does not allow for rash conclusions beyond the fact that psilocybin has significant medical potential and a good safety profile compared to other drugs, given the right context.”
“It is imperative that claims do not get ahead of the state of the evidence. Nevertheless, our findings do suggest that the safe and supervised use of psychedelics under conditions of legalization has the potential to find wide public acceptance. If the field can overcome scientific inaccuracies, pursue rigorous research, and build trust—then psychedelics such as psilocybin may one day be seen as a mainstream means to treat mental illness and possibly also to promote overall well-being.”
The study, “(https://doi.org/10.1080/21507740.2024.2303154) Strong Bipartisan Support for Controlled Psilocybin Use as Treatment or Enhancement in a Representative Sample of US Americans: Need for Caution in Public Policy Persists,” was authored by Julian D. Sandbrink, Kyle Johnson, Maureen Gill, David B. Yaden, Julian Savulescu, Ivar R. Hannikainen, and Brian D. Earp.

(https://www.psypost.org/new-machine-learning-model-could-revolutionize-early-autism-detection/) New machine learning model could revolutionize early autism detection
Nov 2nd 2024, 14:00

A recent study published in (https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2822394) JAMA Network Open introduces an advanced machine-learning model that predicts autism spectrum disorder in young children using limited information, with nearly 80% accuracy for children under two years old. The model, called AutMedAI, was designed to use basic behavioral and medical information that is often available during routine pediatric visits, making it both accessible and practical for wide-scale application in healthcare settings. This model could be instrumental for early autism detection, helping provide necessary interventions sooner to enhance developmental outcomes.
Autism spectrum disorder is a neurodevelopmental condition that affects how individuals perceive and interact with the world around them. It is characterized by challenges in social communication, repetitive behaviors, and limited interests. People with autism may experience difficulties in understanding social cues, forming relationships, and adapting to new environments, with symptoms ranging from mild to severe.
While the causes of autism are complex and involve a mix of genetic and environmental factors, early intervention has been shown to greatly benefit children with autism, particularly in improving social, communication, and behavioral skills. However, diagnosing autism can be challenging, as it often relies on observing specific behaviors that may not fully emerge until after the first few years of life. This has led to a gap between when early signs of autism first appear and when a diagnosis is typically made, delaying potentially helpful interventions.
The motivation behind this study lies in addressing the limitations of current autism screening and diagnostic tools. Traditional screening often relies on questionnaires and checklists, which are useful but can miss subtle signs, may be influenced by interpretation biases, and often require specialized knowledge for accurate assessment. These tools may delay diagnosis, as they typically target children who are already showing pronounced signs of autism, often around age three or later.
Researchers at the Karolinska Institutet in Sweden aimed to develop a more accessible, accurate tool that could identify autism risk in very young children using readily available medical and developmental data. By creating a machine-learning model that analyzes common early-life factors—such as age at first smile or language milestones—they hoped to facilitate earlier identification of autism risk. This early detection could open doors to timely intervention and better developmental support, ultimately improving outcomes for children with autism and their families.
The researchers used data from the SPARK (Simons Foundation Powering Autism Research for Knowledge) database, one of the largest autism research datasets in the United States. The SPARK database includes detailed medical and background information on more than 30,000 children, both with and without autism. For this study, the team focused on a sample of approximately 12,000 children from SPARK to train and validate their machine-learning models. The data was selected to include only information that would typically be available from routine medical visits during a child’s early years, such as age at key developmental milestones and specific behavioral traits.
To build the model, the researchers used 28 distinct factors, carefully chosen to be accessible, non-invasive, and easily reportable by parents. These factors included observable milestones such as when a child first smiled, formed short sentences, or had difficulty with certain foods. The study’s focus was on children under 24 months of age, a critical period for developmental assessment. The team used a variety of machine-learning algorithms, including logistic regression and random forest, to explore different ways to interpret this data. Their best-performing model, AutMedAI, was ultimately chosen after multiple rounds of testing and refinement to maximize predictive accuracy while remaining user-friendly and based on readily available data.
AutMedAI was trained and validated on the SPARK dataset, which was split into multiple subsets to allow for rigorous cross-validation. Specifically, the data was divided so that 60% was used for training, 20% for tuning model parameters, and the remaining 20% for final validation. This method helped ensure that the model was accurate not only within the sample used to train it but also for “unseen” data, mimicking real-world application. The researchers further refined the model by optimizing it to prevent overfitting, ensuring that it could generalize well to new cases.
The AutMedAI model was evaluated on a sample of around 12,000 children and achieved approximately 80% accuracy in predicting autism, correctly identifying a large portion of children who had autism spectrum disorder. The model was particularly effective in flagging children with more profound difficulties in social interaction and cognitive functioning, two areas closely associated with autism.
“The results of the study are significant because they show that it is possible to identify individuals who are likely to have autism from relatively limited and readily available information,” said study first author Shyam Rajagopalan, an affiliated researcher at the Karolinska Institutet and currently an assistant professor at the Institute of Bioinformatics and Applied Technology in India.
Several specific factors emerged as strong predictors within the model, including the age of the child’s first smile, when they began using short sentences, and the presence of eating difficulties. This combination of predictors was both insightful and practical, showing that common developmental milestones could be powerful indicators of autism risk when analyzed collectively.
The researchers emphasized that AutMedAI is not meant to replace detailed clinical assessments but rather to serve as an initial screening tool. By flagging children who may need further evaluation, the model could help ease the strain on diagnostic services and provide families with earlier insights into their child’s development.
Early intervention is especially important for children with autism, as targeted therapies and support systems can significantly improve long-term outcomes, particularly in communication and social skills. The model’s accessibility also holds promise for rural or underserved areas where specialized autism diagnostic services may be less available, offering a valuable option for preliminary screening.
One of the most promising aspects of AutMedAI is its reliance on data that can be gathered without invasive testing or extensive clinical assessments, making it feasible to integrate into routine pediatric care. The researchers plan to conduct further testing and validation in clinical settings to confirm the model’s reliability outside of research environments. They are also exploring the potential to include genetic information in future iterations of the model, which could further improve accuracy and enable even more personalized screening.
“To ensure that the model is reliable enough to be implemented in clinical contexts, rigorous work and careful validation are required. I want to emphasize that our goal is for the model to become a valuable tool for health care, and it is not intended to replace a clinical assessment of autism,” said Kristiina Tammimies, an associate professor at KIND, the Department of Women’s and Children’s Health, Karolinska Institutet and senior author of the study.
The study, “(https://doi.org/10.1001/jamanetworkopen.2024.29229) Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information,” was authored by Shyam Sundar Rajagopalan, Yali Zhang, Ashraf Yahia, and Kristiina Tammimies.

(https://www.psypost.org/white-womens-trump-support-tied-to-racial-resentment-study-finds/) White women’s Trump support tied to racial resentment, study finds
Nov 2nd 2024, 12:00

New findings from the 2020 presidential election show that women voters are far from a political monolith, with attitudes toward race and gender strongly linked to their choices at the ballot box. The research shows that the link between racial attitudes and support for Donald Trump was particularly strong among white women, while hostile sexism appeared to play a bigger role among Latina and Asian American voters. These findings, published in (https://www.cambridge.org/core/journals/politics-and-gender/article/whitewashing-women-voters-intersectionality-and-partisan-vote-choice-in-the-2020-us-presidential-election/7FB8F8938CD16484A6CD53AE017E17F9) Politics & Gender, highlight the diverse and complex factors driving political preferences among women of different racial backgrounds.
The researchers, Chaerim Kim and Jane Junn, were motivated by the observation that most studies on voting behavior often group women together, glossing over how race can shape political preferences within groups of women. They aimed to address what they term “whitewashing”—the tendency to treat women as a homogeneous group despite racial differences that could lead to distinct voting patterns. The 2020 election was a particularly significant case study, given Trump’s polarizing views and rhetoric on both gender and racial issues.
Utilizing data from the 2020 Collaborative Multiracial Post-election Survey (CMPS), the researchers analyzed responses from a diverse group of 8,936 women voters, including Black, Asian American, Latina, and white women. This dataset was particularly useful as it includes large samples of racial and ethnic minorities, allowing the researchers to examine differences among subgroups of women without assuming uniformity within the female electorate.
To gauge how racial and gender attitudes shaped voting behavior, the researchers employed two main scales: the hostile sexism scale and the racial resentment scale. The hostile sexism scale measures antagonistic views toward women (e.g., “Women seek to gain power by getting control over men”). The racial resentment scale, on the other hand, assesses prejudicial attitudes toward racial minority groups, especially focusing on African Americans (e.g., “It is really a matter of some people not trying hard enough; if Blacks would only try harder, they could be just as well off as whites”).
The findings showed significant variation in how racial and gender attitudes influenced voting preferences among the groups of women surveyed, differentiated by race. For white women, racial resentment was a strong predictor of support for Trump. White women who scored higher on the racial resentment scale were much more likely to vote for the Republican candidate, suggesting that racial attitudes were a central factor in their voting decisions.
For Latina voters, racial resentment was associated with a moderate increase in Trump support, suggesting that for some in this group, negative views about other racial groups may have influenced their alignment with conservative positions. Similarly, a subset of Asian American women with higher racial resentment scores also showed slightly greater likelihood of supporting Trump, though this effect was less pronounced than among white women.
In contrast, Black women showed much lower levels of racial resentment, which corresponded with their strong support for Joe Biden in the 2020 election. The findings imply that Black women, whose political choices are often shaped by experiences of racial inequality, were more unified in their voting patterns and less influenced by racial resentment in their decisions.
The study also found that hostile sexism played a unique role among Latina and Asian American women, who were more likely to support Trump if they scored high on the hostile sexism scale. This suggests that attitudes toward traditional gender roles influenced voting behavior within these groups.
These findings indicate that Latina and Asian American women’s voting choices may have been shaped by distinct social expectations related to gender, which differed from the factors that influenced Black women voters. For Black women, racial identity appeared to be a more cohesive and influential factor in voting decisions, while Latina and Asian American women’s choices were influenced by a mix of racial and gender attitudes.
Interestingly, the effect of hostile sexism on voting choice was less pronounced among both Black women and white women. While racial resentment significantly correlated with Trump support among white women, hostile sexism did not have the same strong effect. This difference implies that race, more than gender, influenced white women’s voting decisions, with many prioritizing racial attitudes over gender-based beliefs.
The researchers suggest this could be due to the relative racial privilege white women experience, which allows them more leeway to focus on race-based concerns over gender-related ones. Essentially, as “second in sex to men” but “first in race to minorities,” white women may experience and express a different set of political priorities compared to women of color, who are more frequently subjected to intersecting forms of racial and gender discrimination.
As with all research, there are some limitations. One limitation is its reliance on self-reported data, which can be influenced by social desirability or recall bias. The study also focused on a single election year, and factors like Trump’s unique public image might have shaped voter behavior in ways that may not represent long-term voting patterns.
Future research could examine multiple election cycles to see if these patterns remain consistent over time. Another possible direction for further study could involve exploring other dimensions of identity, such as religion or immigration status, which might also shape how different groups of women approach political decision-making.
The study, “(https://doi.org/10.1017/S1743923X24000345) Whitewashing Women Voters: Intersectionality and Partisan Vote Choice in the 2020 US Presidential Election,” was published September 20, 2024.

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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