Bibliographic record
Abstract
The author argues that social etiology of mental health, which suggests a causal link between living conditions and the occurence of mental disorders, is valid only when one applies a contextual evaluation of psycho-social stress factors. In that case, when life-events are cut from their environmental consequences, they are insufficient in themselves when trying to explain why mental disorders occur. However, once they are evaluated in function of the person's living conditions, the psycho-social stress factors become triggers and key to the person's stability. George W. Brown suggests a psycho-social model for a precipitative agent and for vulnerability factors linked to the significance and the impact of a life-event, relating also to the factual context at its origin and to the client's biographical history. Poverty then becomes a determining background for the life-event's repercussions. The LEDS (Life-Event and Difficulty Schedule) method of contextual analysis is based on a complete and systematical gathering of factual information on the events and on the context, stripped of the client's bias and emotional reactions. Furthermore, the grid of analysis allows one to qualify and organize this information while maintaining an optimal level of precision and objectivity. The empirical demonstration of the contextual analysis' predictive power is convincing in the case of disorders both mental (depression, anxiety, schizophrenia) and physical (infarction, appendicitis, ulcers). In such a frame of analysis and because of the chronic hardships experienced by the underpriveledged, poverty emerges as a determining contextual factor in the social etiology of mental disorders.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".