Adapting a health equity tool to meet professional needs (Québec, Canada)
Bibliographic record
Abstract
While numerous tools are available to better incorporate equity into population health actions, they are limited mainly by their lack of adaptation to professional practices and organizational realities. A study was conducted in Québec to identify and understand, from the perspective of future users, conditions that would facilitate use of a tool (Reflex-ISS) targeted at supporting collaborative action to improve consideration of social inequalities in health (SIH) within population health actions. Concept mapping and focus groups were implemented as complementary methods for investigating the conditions. Significant results that emerged were strong participant interest in the tool and the need for resources to better take SIH into account. The conditions for use that were identified referred to the tool itself (user-friendliness and literacy) and to resources for appropriating the tool, competency development, as well as the role and responsibilities of organizations and policies in promoting use of the tool in daily activities and more fundamentally in acting against SIH in general. Models for organizational innovation give an idea of the dimensions that need to be considered to strengthen the integration of equity into organizations and to support the changes in practice that result from using the tool. They provide a reminder that a health equity tool cannot be the cornerstone of an organizational strategy to fight against SIH; rather, it must be incorporated as part of a systemic strategy of professional and organizational development.
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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".