Stakeholder perspectives on implementing accreditation programs: a qualitative study of enabling factors
Bibliographic record
Abstract
BACKGROUND: Accreditation programs are complex, system-wide quality and safety interventions. Despite their international popularity, evidence of their effectiveness is weak and contradictory. This may be due to variable implementation in different contexts. However, there is limited research that informs implementation strategies. We aimed to advance knowledge in this area by identifying factors that enable effective implementation of accreditation programs across different healthcare settings. METHODS: We conducted 39 focus groups and eight interviews between 2011 and 2012, involving 258 diverse healthcare stakeholders from every Australian State and Territory. Interviews were semi-structured and focused on the aims, implementation and consequences of three prominent accreditation programs in the aged, primary and acute care sectors. Data were thematically analysed to distil and categorise facilitators of effective implementation. RESULTS: Four factors were identified as critical enablers of effective implementation: the accreditation program is collaborative, valid and uses relevant standards; accreditation is favourably received by health professionals; healthcare organisations are capable of embracing accreditation; and accreditation is appropriately aligned with other regulatory initiatives and supported by relevant incentives. CONCLUSIONS: Strategic implementation of accreditation programs should target the four factors emerging from this study, which may increase the likelihood of accreditation being implemented successfully.
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".