Engaging health care professionals in quality improvement: A qualitative study exploring the synergies between projects of professionalisation and institutionalisation in quality improvement collaborative implementation in Denmark
Bibliographic record
Abstract
OBJECTIVE: To examine the projects of professionalisation and institutionalisation forming health care professions' engagement in quality improvement collaborative (QIC) implementation in Denmark, and to analyse the synergies and tensions between the two projects given the opportunities afforded by the QICs. METHODS: This was a cross-sectional interview study with professionals involved in the implementation of two national QICs in Denmark involving 23 individual interviews and focus group discussions with 75 people representing different professional groups. We conducted a reflexive thematic analysis of the data, drawing on institutional contributions to organisational studies of professions. RESULTS: Study participants engaged widely in QIC implementation. This engagement was formed by a constructive interplay between the professions' projects of professionalisation and institutionalisation, with only few tensions identified. The project of professionalisation relates to a self-oriented agenda of contributing professional expertise and promoting professional recognition and development, while the project of institutionalisation focuses on improving health care processes and outcomes and advancing quality improvement. Both projects were largely similar across professional groups. The interplay between the two projects was enabled by the bottom-up approach to implementation, participation of QI specialists, and a clear focus on developing and delivering high-quality patient care. CONCLUSIONS: Future strategies for QIC implementation should position QICs as a framework that promotes the integration of professions' projects of professionalisation and institutionalisation to successfully engage professionals in the implementation process, and thereby optimise the effectiveness of QICs in 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 | 0.039 | 0.041 |
| 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 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 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".