Equity reporting: a framework for putting knowledge mobilization and health equity at the core of population health status reporting
Bibliographic record
Abstract
The National Collaborating Centres for Public Health (NCCPH) collaborated on the development of an action framework for integrating equity into population health status reporting. This framework integrates the research literature with on-the-ground experience collected using a unique collaborative learning approach with public health practitioners from across Canada. This article introduces the Action Framework, describes the learning process, and then situates population health status reporting (PHSR) in the current work of the public health sector. This is followed by a discussion of the nature of evidence related to the social determinants of health as a key aspect of deciding what and how to report. Finally, the connection is made between data and implementation by exploring the concept of actionable information and detailing the Action Framework for equity-integrated population health status reporting. The article concludes with a discussion of the importance of putting knowledge mobilization at the core of the PHSR process and makes suggestions for next steps. The purpose of the article is to encourage practitioners to use, discuss, and ultimately strengthen the framework.
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 | 0.229 | 0.111 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.011 |
| Science and technology studies | 0.019 | 0.104 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.010 | 0.026 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".