Bibliographic record
Abstract
In addition to this month’s In Review,1–3 the current issue of The Canadian Journal of Psychiatry contains 2 articles on suicide. Dr Angela Onkay Ho4 draws on historical, philosophical, and legal arguments to explore the question of whether suicide can be rational. Dr Anne E Rhodes and colleagues5 present data on time trends for suicide-related behaviours in Ontario. At first glance, the 2 papers seem to have little in common: 1 of them pursues a narrative exploration and the other an epidemiologic analysis. However, similarities lurk below the surface. Can suicide occur in the absence of, or be uninfluenced by, a psychiatric disorder? Using terminology familiar to epidemiologists, this is a question of whether psychiatric disturbances are a necessary cause of suicide. It would be very surprising if the answer was yes. With the exception of infectious diseases, very few contemporary health problems have necessary causes. Rather, almost all are determined by multiple component causes that interact with each other to form complex causal mechanisms. Mental disorders, in themselves, are neither necessary nor sufficient causes of suicide, but are examples of component causes. Primary prevention addresses component causes. The epidemiologic logic of causal reasoning aligns well with that of clinical practice, although a different terminology predominates. The language of clinical practice uses familiar terms, such as “predisposing” and “precipitating” factors, “diathesis” and “vulnerability,” rather than the component cause terminology typically favoured by epidemiologists. However, both chafe against the more blunt reasoning associated with practice guidelines, and medicolegal anxieties. Regulatory decisions are also blunt instruments that can fall prey to simplified etiological assumptions. Discouraging the use of antidepressants in adolescents will reduce the risk of suicidal thoughts and actions, if antidepressants are a component cause of these outcomes, but the same regulatory warning may increase exposure to another component cause: untreated depression. The quantification of real-world risk, as provided by Dr Rhodes and colleagues’ study,5 is therefore of critical importance. Guidelines and regulatory actions, no matter how sophisticated, are all based on generalities and cannot be expected to determine clinical decisions in all circumstances. Ultimately, the uniqueness of each clinical situation is something that clinicians and their patients need to address together within the societal and legal context they share. A “delicate consideration of clinical judgment, duty of care, and legal obligations”4, p 141 seems like a good place to start.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.227 | 0.475 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.020 | 0.040 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".