Comparison of health service accreditation programs in low- and middle-income countries with those in higher income countries: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: The study aim was twofold: to investigate and describe the organizational attributes of accreditation programmes in low- and middle-income countries (LMICs) to determine how or to what extent these differ from those in higher-income countries (HICs) and to identify contextual factors that sustain or are barriers to their survival. DESIGN: Web-based questionnaire survey. PARTICIPANTS: National healthcare accreditation providers and those offering international services. In total, 44 accreditation agencies completed the survey. MAIN OUTCOME MEASURE(S): Income distinctions, accreditation programme features, organizational attributes and cross-national divergence. RESULTS: Accreditation programmes of LMICs exhibit similar characteristics to those of HICs. The consistent model of accreditation worldwide, centres on promoting improvements, applying standards and providing feedback. Where they do differ, the divergence is over specialized features rather than the general logic. LMICs were less likely than HICs to include an evaluation component to programmes, more likely to have certification processes for trainee surveyors and more likely to make decisions on the accreditation status based on a formulaic, mathematically oriented approach. Accreditation programme sustainability, irrespective of country characteristics, is influenced by ongoing policy support from government, a sufficient large healthcare market size, stable programme funding, diverse incentives to encourage participation in accreditation by Health Care Organizations as well as the continual refinement and improvement in accreditation agency operations and programme delivery. CONCLUSIONS: Understanding the similarities, differences and factors that sustain accreditation programmes in LMICs, and HICs, can be applied to benefit programmes around the world. A flourishing accreditation programme is one element of the institutional basis for high-quality 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".