Understanding the impact of accreditation on quality in healthcare: A grounded theory approach
Bibliographic record
Abstract
OBJECTIVE: To explore how organizations respond to and interact with the accreditation process and the actual and potential mechanisms through which accreditation may influence quality. DESIGN: Qualitative grounded theory study. SETTING: Organizations who had participated in Accreditation Canada's Qmentum program during January 2014-June 2016. PARTICIPANTS: Individuals who had coordinated the accreditation process or were involved in managing or promoting quality. RESULTS: The accreditation process is largely viewed as a quality assurance process, which often feeds in to quality improvement activities if the feedback aligns with organizational priorities. Three key stages are required for accreditation to impact quality: coherence, organizational buy-in and organizational action. These stages map to constructs outlined in Normalization Process Theory. Coherence is established when an organization and its staff perceive that accreditation aligns with the organization's beliefs, context and model of service delivery. Organizational buy-in is established when there is both a conceptual champion and an operational champion, and is influenced by both internal and external contextual factors. Quality improvement action occurs when organizations take purposeful action in response to observations, feedback or self-reflection resulting from the accreditation process. CONCLUSIONS: The accreditation process has the potential to influence quality through a series of three mechanisms: coherence, organizational buy-in and collective quality improvement action. Internal and external contextual factors, including individual characteristics, influence an organization's experience of accreditation.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".