Primary healthcare accreditation standards: a systematic review
Bibliographic record
Abstract
PURPOSE: Accreditation is an essential component in primary healthcare (PHC) systems. The purpose of this paper is to investigate the most suitable PHC accreditation models and standards, worldwide, and to prepare a comprehensive and unbiased summary from research on these models. DESIGN/METHODOLOGY/APPROACH: A systematic search was undertaken using Web of Science, Scopus, Science Direct, Springer, PubMed and ProQuest databases in August 2016 and updated in January 2018. English language studies addressing PHC accreditation standards and models, published between 1995 and January 2018, were included, resulting in 9051 citations. After excluding duplicates and irrelevant studies, 19 were included in the final review. Two independent reviewers critically appraised the studies. Consequently, accreditation standards in the models were extracted and compared. FINDINGS: Results indicate that USA, Australia, Canada, UK and New Zealand (non-eastern Mediterranean regions (EMR)) and Jordan, Saudi Arabia, Lebanon and Egypt (EMR) had well-developed and high-quality PHC accreditation models. The Jordanian, Egyptian and Saudi models had the highest diversity in their PHC standards domains. Community-oriented care, safe care, high-quality care, care continuity and human resource management had the highest priority among PHC accreditation programs. ORIGINALITY/VALUE: The authors provide PHC accreditation benchmarks and determine high priority practical domains in accreditation standards. The findings should help health system managers and policymakers design new PHC accreditation programs and promote PHC service quality.
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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 | 0.047 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".