Adaptation et conditions d’utilisation d’un outil d’analyse des interventions au regard des inégalités sociales de santé
Bibliographic record
Abstract
Although actions to reduce social inequalities in health cannot be considered the exclusive responsibility of public health actors, they should at least make sure their interventions account for these inequalities. However, the actors involved in these interventions have few tools to support them in this process. Therefore, building on a study conducted in France, we have adapted, tested, and developed in Quebec a tool intended to help actors take into account social inequalities in health. The article presents the approach that led to the adaptation of the tool to the Quebec context, to describe the tool, and then to discuss some issues for inclusion in professional practices. A participatory and constructive process between researchers, managers and practitioners led to a useful and useable tool. It is composed of five aspects of intervention (planning, implementation, evaluation, sustainability, and empowerment) and 44 items for discussion presented as questions. A user guide, a glossary, and some practical examples accompany the tool. It follows a reflexive and constructive process wherein a third party facilitator can assist actors involved in an intervention to analyze how they take social inequalities in health into account. This assessment can help generate collective recommendations for improvements, which can be monitored over time, to improve consideration of equity in public health interventions. The article concludes on some issues related to its integration into professional practices.
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 | 0.265 | 0.345 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".