Social Determinants of Health: So What?
Bibliographic record
Abstract
I have used this column on previous occasions to draw atten-tion to the social determinants of health (SD). Although sev-eral Canadian researchers have made significant contributions to our understanding of SD and although the WHO Commis-sion on Social Determinants of Health and the first annual report of Canada’s Chief Public Health Officer focused specif-ically on SD, the broader public health (PH) community has been slow to embrace the concept. Although the importance of SD is recognized, why have calls to governments and society at large to take steps to “close the inequity gap in a generation ” so far induced little excitement and mobilization in our community? There are many expla-nations for this inertia, including the current economic reces-sion, the perceived absence of levers to act by local public health professionals and the absence of a dominant public
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".