[A framework to support action in population mental health].
Bibliographic record
Abstract
In Quebec, like elsewhere in the world, we are witnessing a growing concern for the population's mental health and for the importance of concentrating efforts on prevention and promotion. In this context, public health actors are invited to adopt a leadership role in advancing mental health promotion and mental disorder prevention goals, and establish the required partnerships with actors from the health and social services and from other sectors who are indispensable to the population mental health agenda. In Canada, public heath actors are not yet sufficiently supported in this role. They express the need to access structuring frameworks which can clarify their action in mental health. This article first presents the momentum for change at the policy level within the field of mental health. A framework to support population mental health action is then presented. The framework identifies the various dimensions underlying the promotion of population mental health as well as the reduction of mental health inequalities. The article finally illustrates how the application of a populational (the application of a populational responsibility perspective) responsibility perspective, as it is defined in the context of Quebec, facilitates the implementation of the various elements of this framework. In the end, public health actors are better equipped to situate their practice in favour of the population's mental health.
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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 | 0.020 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.037 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".