Profiling health-care accreditation organizations: an international survey
Bibliographic record
Abstract
OBJECTIVE: To describe global patterns among health-care accreditation organizations (AOs) and to identify determinants of sustainability and opportunities for improvement. DESIGN: Web-based questionnaire survey. PARTICIPANTS: Organizations offering accreditation services nationally or internationally to health-care provider institutions or networks at primary, secondary or tertiary level in 2010. MAIN OUTCOME MEASURE: s) External relationships, scope and activity public information. RESULTS: Forty-four AOs submitted data, compared with 33 in a survey 10 years earlier. Of the 30 AOs that reported survey activity in 2000 and 2010, 16 are still active and stable or growing. New and old programmes are increasingly linked to public funding and regulation. CONCLUSIONS: While the number of health-care AOs continues to grow, many fail to thrive. Successful organizations tend to complement mechanisms of regulation, health-care funding or governmental commitment to quality and health-care improvement that offer a supportive environment. Principal challenges include unstable business (e.g. limited market, low uptake) and unstable politics. Many organizations make only limited information available to patients and the public about standards, procedures or results.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".