Guest Editorial Suicide: The Persisting Challenge
Bibliographic record
Abstract
In Canada, 4074 persons took their lives in 1999, the latestyear for which suicide data are available. This equates with age-standardized death rates of 21 and 5 per 100 000 for males and females, respectively, and of 13 per 100 000 for the sexes combined. Suicide remains a grave problem in this country; it burgeoned during the late 1960s and 1970s, peaked in the early 1980s, and plateaued just below its peak during the 1990s. Although female suicide rates have decreased, suicide stubbornly remains at this level for males (who comprise four-fifths of suicides) (Figure 1). Contrast this with Scandi-navian countries such as Sweden, where suicide rates de-creased 28 % between 1991 and 1997 in parallel with the increased use of antidepressants. In no demographic subgroup in Sweden did suicide rates fall without a corresponding in-crease in the use of antidepressants. Further, this decrease was not apparently related to changes in unemployment rates or al-
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".