Competition in collaborative clothing: a qualitative case study of influences on collaborative quality improvement in the ICU
Bibliographic record
Abstract
BACKGROUND: Multiorganisational quality improvement (QI) collaborative networks are promoted for improving quality within healthcare. Recently, several large-scale QI initiatives have been conducted in the intensive care unit (ICU) environment with successful quantitative results. However, the mechanisms through which such networks lead to QI success remain uncertain. We aim to understand ICU staff perspectives on collaborative QI based on involvement in a multiorganisational improvement network and hypothesise about theoretical constructs that might explain the effect of collaboration in such networks. METHODS: Qualitative study using a modified grounded theory approach. Key informant interviews were conducted with staff from 12 community hospital ICUs that participated in a cluster randomized control trial (RCT) of a QI intervention using a collaborative approach between 2006 and 2008. Data analysis followed the standard procedure for grounded theory using constant comparative methodology. RESULTS: The collaborative network was perceived to promote increased intrateam cooperation over interorganisational cooperation, but friendly competition with other ICUs appeared to be a prominent driver of behaviour change. Bedsides, clinicians reported that belonging to a collaborative network provided recognition for the high-quality patient care that they already provided. However, the existing communication structure was perceived to be ineffective for staff engagement since it was based on a hierarchical approach to knowledge transfer and project awareness. CONCLUSIONS: QI collaborative networks may promote behaviour change by improving intrateam communication, fostering competition with other institutions, and increasing recognition for providing high-quality care. Other commonly held assumptions about their potential impact, for instance, increasing interorganisational legitimisation, communication and collaboration, may be less important.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".