Adversity Over the Life Course: A Comparison Between Women and Men Who Died by Suicide
Bibliographic record
Abstract
Purpose: This study sets out to compare the presence of life events across different domains throughout the life course which may contribute to the burden of adversity experienced differently among men and women who died by suicide. Method: In a sample of 303 individuals (213 men and 90 women), data was derived from extensive clinical interviews conducted with informants. Models allowed the identification of patterns of life trajectories. Results: Overall, the burden of adversity was similar across the life course except for the 5–9, 25–29, and 30–34 age ranges, where a significant difference appeared between genders [t-test = 2.13 (p < 0.05), 2.16 (p < 0.05) and 3.08 (p < 0.005), respectively] that seems to disadvantage women. The early adversities of violence and neglect, between 0 and 19 years old, are important for both groups. During the life course, women were more exposed to interpersonal adverse events such as being victims of negligence and violence, relational difficulties or abuse from their spouse, as well as tension with their own children. Men encountered more academic difficulties, legal entanglements and financial difficulties, and were more than three times more likely to develop an alcohol/drug abuse problem than women. Conclusions: The data suggests some gender differences in exposure to longstanding and severe life problems contributing to suicide vulnerability. For women, the continuing burden emerges from chronic interpersonal adversities, whereas, for men, the adverse events are to a larger degree socially exposed, compounded with alcohol misuse. The adversities, especially those of a public or social nature, may be witnessed by others, which should favor the detection of vulnerability over the life course, and psychosocial or mental health services should be offered and provided earlier during the life course. Yet more men die by suicide than women. Resiliency and protective factors may benefit women to a greater degree. Future research should tackle the challenge of investigating these important elements. Meanwhile, from a public health perspective, access to psychosocial and mental health services and social acceptability of seeking services should be part of an ongoing effort in all institutional structures as a way of decreasing downstream mental health problems and vulnerability to suicide.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".