Sentinels of inequity: examining policy requirements for equity-oriented primary healthcare
Bibliographic record
Abstract
BACKGROUND: Non-government, not-for-profit community health centres (CHCs) play a crucial role within healthcare systems in fostering equity, acting both as direct providers of services and as sentinels of health and social inequity. In a study of an intervention to promote equity-oriented health care, we enlisted four diverse primary healthcare clinics with mandates to serve highly marginalized populations. All of these CHCs operate as not-for-profit, non-government organizations (NGOs), and have a marginal relationship financially and socially to other parts of the system. The purpose of this paper is to provide an analysis of the factors that shape how CHCs are able to carry out an equity mandate and, from this, to identify what is required at the level of policy to enhance capacity to provide equity-oriented health care. METHODS: We systematically examined the clinics' policy and funding contexts, and identified influences on the clinics' capacities to promote equity-oriented health care. RESULTS: We identified three key mechanisms of influence, each playing out against the backdrop of a contested and marginal position of CHCs within the health care system: a) accountability and performance frameworks; b) patterns of funding and allocation of resources, and c) pathways for emergent priorities. We examine these mechanisms, considering how each influenced the pursuit of equity, and propose policy directions to optimize the primary health care sectors' capacity to support equity-oriented health care. CONCLUSIONS: Although this analysis is based on a study within a high-income country, we argue that because the dynamics between community health centres and broader healthcare systems are similar across national boundaries, the implications have applicability to low and middle-income countries.
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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 | 0.041 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".