Do great local minds think alike? Comparing perceptions of the social determinants of health between non-profit and governmental actors in two Canadian cities
Bibliographic record
Abstract
Cities are important sites for intervention on social determinants of health (SDOH); yet, little is known about how influential local actors, namely workers in municipal governments (GOVs) and community-based organizations (CBOs), perceive the SDOH. Capturing and comparing perceptions between these groups are important for assessing how SDOH discourse has permeated local actors' thinking--a meaningful endeavour as local-level health equity action often invokes inter-institutional partnerships. This paper compares SDOH perceptions between CBO workers in Hamilton, Ontario, with politicians and senior-level staff in GOVs in Vancouver, British Columbia, based on two studies with surveys containing identical questions on SDOH perceptions. Overall, there was high comparability between the groups in their relative ratings of the SDOH. Both groups assigned high levels of 'influence' and 'priority' to 'healthy lifestyles' and 'clean air and water' and lower levels to 'strong community' and 'income'. Given the importance of a shared vision in collaborative enterprises, the comparability of perceptions between the groups found here holds promise for the prospect of inter-institutional partnerships. However, the low rating assigned to more structural health determinants suggests that more work is needed from researchers and advocates to effectively advance a health equity agenda at the local level in Canada.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".