Indicators to guide health equity work in local public health agencies: a locally driven collaborative project in Ontario
Bibliographic record
Abstract
INTRODUCTION: Funded by a Public Health Ontario 'Locally Driven Collaborative Project' grant, a team led by public health practitioners set out to develop and test a comprehensive set of indicators to guide health equity work in local public health agencies (LPHAs). METHODS: The project began with a scoping review, consultation with content experts, and development of a face-validated set of indicators aligned with the four public health roles to address health inequities (NCCDH, 2014), plus a fifth set of indicators related to an organizational and system development role. We report here on the field testing of the indicators for feasibility, face validity (clarity, relevance), reliability, and comparability in four Ontario LPHAs. Data were collected by two separate individuals or groups at each site, during two consecutive periods. These individuals participated in separate focus groups at the end of each test period, which further examined indicator clarity, data source availability and relevance. A third focus group explored anticipated indicator uses. RESULTS: Field testing showed that indicators addressed important issues in all public health roles. Although the capacity for indicator use varied, all test sites found the indicators useful. Suggestions for improved clarity were used to refine the final set of indicators, and to develop a Health Equity Indicator User Guide with background information and recommended resources. CONCLUSION: The process of evaluating health equity-related activity within LPHAs is still in its early stages. This project provides Ontario LPHAs with a tool to guide health equity work that may be adaptable to other Canadian jurisdictions.
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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 | 0.042 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".