Child maltreatment as predictors of suicidal ideas and attempts in a general female population
Bibliographic record
Abstract
Child maltreatment has been investigated as a suicide risk for decades. The aim of this study was to evaluate the prevalence of women from the general population with suicidal ideas or attempts and add to the actual literature a larger perspective of different types of maltreatment in regards to experiences such as neglect, psychological, physical or sexual abuse, and its association to risk factor for suicide ideas and attempts. Data were collected during a telephone survey held between March and April, 2009 among a sample of 1,001 female adult respondents from the province of Quebec (Canada). Questions were selected to investigate childhood maltreatment as a risk factor for probable depression, and actual post-traumatic stress disorders, and suicidal behaviours in the course of their lives. Regression analysis indicates a positive association between sexual abuse and suicidal ideations, as well as a positive association between sexual abuse, psychological abuse, probable depression and suicide attempts. Respondents, who attempted suicide, were two to three times more likely to have experienced the presence of sexual or psychological abuse in the past and four times more likely to have been screened for a probable depression. Interventions that target the early reduction of sexual or psychological abuse, may translate into ulterior reduction in mental health and suicidal behaviours. Key words: Suicide attempt, suicidal ideas, child maltreatment, neglect, abuse.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".