Disrupting activities in quality improvement initiatives: a qualitative case study of the QuICR Door-To-Needle initiative
Bibliographic record
Abstract
BACKGROUND: Healthcare quality improvement (QI) efforts are ongoing but often create modest improvement. While knowledge about factors, tools and processes that encourage QI is growing, research has not attended to the need to disrupt established ways of working to facilitate QI efforts. OBJECTIVE: To examine how a QI initiative can disrupt professionals' established way of working through a study of the Alberta Stroke Quality Improvement and Clinical Research (QuICR) Door-to-Needle Initiative. DESIGN: A multisite, qualitative case study, with data collected through semistructured interviews and focus groups. Inductive data analysis allowed findings to emerge from the data and supported the generation of new insights. FINDINGS: In stroke centres where improvements were realised, professionals' established understanding of the clinical problem and their belief in the adequacy of existing treatment approaches shifted-they no longer believed that their established understanding and treating the clinical problem were appropriate. This shift occurred as participants engaged in specific activities to improve quality. We identify these activities as ones that create urgency, draw professionals away from regular work and encourage questioning about established processes. These activities constituted disrupting action in which both clinical and non-clinical persons were engaged. CONCLUSIONS: Disrupting action is an important yet understudied element of QI. Disrupting action can be used to create gaps in established ways of working and may help encourage professionals' involvement and support of QI efforts. While non-clinical professionals can be involved in disrupting action, it needs to engage clinical professionals on their own terms.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.032 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".