L’apport de la recherche en psychologie dans la compréhension complexe de l’étiologie du suicide
Bibliographic record
Abstract
If certain risk factors are known to increase suicidal behaviors, the question is to determine the differential weight of these various risk factors, on which individuals, in which context and in what period of their lives? We have put to test a model that explains different life trajectories leading to suicide. This research allows to surpass a correlation model of identification of risk factors and to target four distinct sub-groups of individuals for whom the developmental history seems quite different. It is clear that suicide is a complex, multidimensional and multilevel issue. Being at the crossroads of many scientific disciplines, psychology may help integrate and connect knowledge with other disciplines in order to clarify the contexts that affect suicidal individuals differently. This knowledge may help in identifying specific prevention interventions that could modify this chain of events leading ultimately to suicide.
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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".