Measurement and rural primary health care: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Primary health care (PHC) is the foundation of healthcare systems around the world, recognized for its ability to deliver cost-effective, equitable, and high-quality care. Measuring and reporting on PHC performance allows decision-makers to ensure accountability and quality improvement. Rural areas, where residents are few and widely dispersed across vast areas, present special challenges for PHC delivery, and performance measurement systems need to acknowledge the ways rural PHC is unique. The objective of this scoping review is to establish the features of PHC that should be measured and reported in a rural versus a non-rural context. METHODS: The electronic databases PubMed, Scopus, and CINAHL, as well as grey literature in the form of government reports and research institute publications, were searched for relevant studies. Identified articles were eligible for inclusion if they reported or described (1) rural primary health care; (2) healthcare practice characteristics or structures, provider scope of practice, provider practice patterns, or patient patterns of health care use; and (3) one of four 'pillars' of quality PHC outlined in the College of Family Physicians of Canada's 'Patient's Medical Home' model: accessibility, continuity, comprehensiveness, or electronic health records. Articles were excluded if they reported or described (1) specific patient populations, health concerns, or health outcomes; or (2) patient preferences or experiences with PHC. Data were extracted and analyzed to determine unique aspects of rural PHC. Twenty-six articles met inclusion criteria. RESULTS: Results suggest important differences in aspects of rural PHC, particularly in how rural patients access such care and the types of services they receive from providers compared to non-rural patients. CONCLUSION: These differences between rural and non-rural PHC will need to be considered in the design of performance measurement systems. Key words: Canada, health reporting, performance measurement, primary health care.
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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 | 0.030 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.027 | 0.039 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".