Addressing Health Inequities in Environmental Public Health in Alberta
Bibliographic record
Abstract
Environmental health related inequities can occur when environmental hazards or disasters disproportionally impact vulnerable populations, when environmental protection activities place a disproportionate burden on marginalized groups through a lack of inclusion or representation, and through creation of policies or programs that address only the immediate environment rather than the broader structural determinants that have created it ( Gore and Anita, 2013 ). Environmental public health (EPH) practitioners are well positioned to reduce inequities when they are empowered to include an equity lens in their work and identify opportunities to act on the social determinants of health (SDH). This focus group project identified ways in which public health inspectors in Alberta Health Services Calgary zone understand the concepts of equity and SDH as relevant to their work, revealed gaps in understanding and practice, and generated ideas to operationalize the integration of an equity lens into EPH practice. This project helps reinforce the importance of providing health equity education and opportunities for collaboration as a catalyst for action to integrate SDH and health equity into professional competencies and address organizational/operational barriers. Sharing these results will be helpful in moving towards fulfilling the key inequity-reducing role of EPH practice.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".