Enhancing measurement of primary health care indicators using an equity lens: An ethnographic study
Bibliographic record
Abstract
INTRODUCTION: One important goal of strengthening and renewal in primary healthcare (PHC) is achieving health equity, particularly for vulnerable populations. There has been a flurry of international activity toward the establishment of indicators relevant to measuring and monitoring PHC. Yet, little attention has been paid to whether current indicators: 1) are sensitive enough to detect inequities in processes or outcomes of care, particularly in relation to the health needs of vulnerable groups or 2) adequately capture the complexity of delivering PHC services across diverse groups. The purpose of this paper is to contribute to the discourse regarding what ought to be considered a PHC indicator and to provide some concrete examples illustrating the need for modification and development of new indicators given the goal of PHC achieving health equity. METHODS: Within the context of a larger study of PHC delivery at two Health Centers serving people facing multiple disadvantages, a mixed methods ethnographic design was used. Three sets of data collected included: (a) participant observation data focused on the processes of PHC delivery, (b) interviews with Health Center staff, and (c) interviews with patients. RESULTS: Thematic analysis suggests there is a disjuncture between clinical work addressing the complex needs of patients facing multiple vulnerabilities such as extreme levels of poverty, multiple chronic conditions, and lack of housing and extant indicators and how they are measured. Items could better measure and monitor performance at the management level including, what is delivered (e.g., focus on social determinants of health) and how services are delivered to socially disadvantaged populations (e.g., effective use of space, expectation for all staff to have welcoming and mutually respectful interactions). New indicators must be developed to capture inputs (e.g., stability of funding sources) and outputs (e.g., whole person care) in ways that better align with care provided to marginalized populations. CONCLUSIONS: The current emphasis on achieving greater equity through PHC, the continued calls for the renewal and strengthening of PHC, and the use of monitoring and performance indicators highlight the relevance of ensuring that there are more accurate methods to capture the complex work of PHC organizations.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".