Isolating unique variance in mental health outcomes attributable to personality variables and childhood emotional abuse
Bibliographic record
Abstract
Introduction: University students are at high risk for anxiety and depression. Our main objective was to tease apart variance in symptom severity that was uniquely attributable to four associated variables that are frequently confounded: exposure to childhood emotional abuse, alexithymia, sensory processing sensitivity (SPS), and anxiety sensitivity (AS). Methods: = 410) completed an online survey designed to measure our four key study variables along with several other potentially relevant variables including sex, physical activity levels, and perceived COVID-19 impacts. Results: Over half of the participants reported moderate to extremely severe symptoms of anxiety and depression. Females reported stronger signs of SPS and AS and were more likely than males to have increased their moderate/vigorous exercise since the pandemic began. After controlling for the other variables, the best predictors of perceived COVID-19 impacts were SPS, childhood emotional abuse, and current levels of physical activity. Whereas all three personality variables and childhood emotional abuse emerged as significant predictors of both depression and anxiety, neither COVID-19 impacts nor physical activity levels accounted for unique variance in either model. Unexpectedly, male sex emerged as an additional risk factor for depression, raising the possibility that males experience unique stressors and societal pressures that increase their risk of depression. Discussion: These findings help to clarify the links between childhood emotional abuse, personality traits implicated in emotional awareness and self-regulation, and mental health. They may have important implications for the development and implementation of individualized treatments for common mental disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".