Gender Differences in Youth Suicide and Healthcare Service Use
Bibliographic record
Abstract
BACKGROUND: Healthcare service use among suicide decedents must be well characterized and understood since a key strategy for preventing suicide is to improve healthcare providers' ability to effectively detect and treat those in need. AIMS: To determine gender differences in healthcare service use 12 months prior to suicide. METHOD: Data for 1,231 young Quebec residents (≤ 25 years) who died by suicide between 2000 and 2007 were collected from public health insurance agency databases and coroner registers. Healthcare visits were categorized according to the setting (emergency department [ED], outpatient, and hospital) and their nature (mental health vs. non-mental health). RESULTS: Girls were more likely than boys (82.5% vs. 74.9%, p = .011) to have used healthcare services in the year prior to death. A higher proportion of girls had used outpatient services (79.0% vs. 69.5%, p = .003), had been hospitalized (25.7% vs. 15.6%, p < .001) and had received a mental health-related diagnosis (46.7% vs. 33.1%, p < .001). However, no gender differences were observed in ED visits (59.5% vs. 54.5%, p = .150). CONCLUSION: There is an important proportion of suicide decedents who did not receive a mental health diagnosis and healthcare services in the year prior to death. Future studies should focus on examining gender-specific individual and health system barriers among suicide decedents as well as the quality of care offered regarding detection and treatment.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".