Factors Associated with Not Seeking Professional Help or Disclosing Intent Prior to Suicide: A Study of Medical Examiners' Records in Nova Scotia
Bibliographic record
Abstract
OBJECTIVE: Individual-level data from clinical settings lack information on people who did not seek professional help prior to suicide. We used records of the Nova Scotia Medical Examiner Service (NSMES) to compare people who had contact with a health professional prior to suicide with those who did not. METHOD: We linked data from the NSMES to routine administrative data of the province. RESULTS: The NSMES recorded 108 suicides in Nova Scotia from January 1, 2006, to December 31, 2006; there were 90 male and 18 female suicide deaths. Mean and median age at death were 44.73 (SD 13.33) and 44 years, respectively. Patients aged 40 to 49 years made up one-third of the cases (n = 35) and this was the decade of life with the highest number of suicides. This was also the group least likely to have suicidal intent recorded in the NSMES files (χ(2) = 3.86, df = 1, P = 0.05). Otherwise, there were no significant differences between people who sought help, or disclosed intent, prior to suicide and people who did not. The samples in all cases were predominately male and single. CONCLUSIONS: People aged 40 to 49 years were the age group with the highest absolute number of suicides, but were the least likely to have suicidal intent recorded in the NSMES files. This finding merits further investigation. Medical examiner or coroner data may provide additional information not obtained elsewhere for the surveillance of 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".