Treating Suicidality in Depressive Illness. Part I: Current Controversies
Bibliographic record
Abstract
OBJECTIVE: To consider 2 current controversies about the treatment of depressed suicidal patients: 1) whether they are undertreated by physicians and 2) whether the increase in antidepressant prescribing in recent years is responsible for the observed fall in suicide rates in some countries. METHOD: Systematic review of the literature. Analysis of Canadian data. RESULTS: There is evidence that depressive illness in suicidal persons has been undertreated, but there has been improvement, possibly in response to enhanced public and professional awareness. There continues to be public resistance to taking antidepressants and (among men) to seeking professional help for suicidal depression. Because of inconsistencies, the epidemiologic evidence (although sometimes compelling) remains inconclusive for attributing the decline in suicide rates in some countries to the increase in new-generation antidepressants supply. CONCLUSIONS: Physicians should aggressively pursue recognition and treatment of depression and suicidality but not put their entire faith in medication. Suicide is the product of complex factors that become the person's individual predicament, some of them beyond the capacity of antidepressant drugs to control. The physician should endeavour to assist the patient either to alter the personal predicament or to come to terms with it, as well as prescribing medication appropriately.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".