Reducing health inequities: the contribution of core public health services in BC
Bibliographic record
Abstract
BACKGROUND: Within Canada, many public health leaders have long identified the importance of improving the health of all Canadians especially those who face social and economic disadvantages. Future improvements in population health will be achieved by promoting health equity through action on the social determinants of health. Many Canadian documents, endorsed by government and public health leaders, describe commitments to improving overall health and promoting health equity. Public health has an important role to play in strengthening action on the social determinants and promoting health equity. Currently, public health services in British Columbia are being reorganized and there is a unique opportunity to study the application of an equity lens in public health and the contribution of public health to reducing health inequities. Where applicable, we have chosen mental health promotion, prevention of mental disorders and harms of substance use as exemplars within which to examine specific application of an equity lens. METHODS/DESIGN: This research protocol is informed by three theoretical perspectives: complex adaptive systems, critical social justice, and intersectionality. In this program of research, there are four inter-related research projects with an emphasis on both integrated and end of grant knowledge translation. Within an overarching collaborative and participatory approach to research, we use a multiple comparative case study research design and are incorporating multiple methods such as discourse analysis, situational analysis, social network analysis, concept mapping and grounded theory. DISCUSSION: An important aim of this work is to help ensure a strong public health system that supports public health providers to have the knowledge, skills, tools and resources to undertake the promotion of health equity. This research will contribute to increasing the effectiveness and contributions of public health in reducing unfair and inequitable differences in health among population groups. As a collaborative effort between public health practitioners/decision makers and university researchers, this research will provide important understanding and insights about the implementation of the changes in public health with a specific focus on health equity, the promotion of mental health and the prevention of harms of substance use.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".