Comparing Childhood Characteristics of Adopted and Non-adopted Individuals Deceased by Suicide
Bibliographic record
Abstract
Objective: Across the globe more than 35,000 children a year are adopted by non-relatives, and some studies suggest that adopted individuals may be more vulnerable to developing mental disorders. To map the differences in suicide risk factors in adopted and non-adopted individuals, this study will compare the development of mental disorders as well as life events occurring before the age of 18 for both adopted and non-adopted individuals deceased by suicide. Methods: This study included 13 adopted and 26 non-adopted individuals deceased by suicide as well as 26 non-adopted living control individuals. Cases were taken from a data bank created over the last decade by researchers of [our institution] comprising a mixture of 700 suicide cases and living control individuals aged from 14 to 84. Adopted and non-adopted individuals deceased by suicide; adopted individuals deceased by suicide and non-adopted living control individuals were each compared on Axis I and II disorders, early life events, and burdens of adversity. Results: Results show significant differences, with a higher rate of Attention Deficit Hyperactivity Disorder, mental health comorbidity and Cluster C personality disorders among adopted individuals. Furthermore, adopted individuals have higher adversity scores prior to the age of 15. Conclusion: This study underlines the fact that adoptive families need to be supported throughout adoption. Health care professionals need specialized training on this matter, and the psychological challenges adopted individuals face need to be treated at the earliest juncture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".