Perceptions about Equity in Public Health: A comparison between frontline staff and informing policy in Ontario
Bibliographic record
Abstract
Background: Recent changes in Ontario public health policy call for increased emphasis on equity. However, it is not clear how equity is understood as a concept and how equity is understood as practice. Methods: The aim of this study was to understand public health frontline staff (FLS) perspectives on health equity and how these align with provincial public health policy documents. A qualitative content analysis design was used to examine transcripts from six focus group interviews with frontline public health workers and seven key provincial public health documents that have shaped or influenced public health program planning in Ontario. Perceptions and understandings of health equity in public health were compared. Results: Findings from the study indicate that several areas of alignment exist between how FLS describe equity in public health practice and how equity is addressed in the provincial policy documents; both focus their discussion of equity as relating to the social determinants of health and priority populations. Several differences between FLS perspectives and policy documents were also identified including barriers encountered in FLS daily practice that are not addressed in the provincial policy documents. Conclusions: These alignments and differences provide insights on how FLS incorporate information from provincial policy documents into their practice and suggest the importance of involving FLS in the policy process.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".