Exploring role clarity in interorganizational spread and scale-up initiatives: the ‘INSPIRED’ COPD collaborative
Bibliographic record
Abstract
BACKGROUND: Role clarification is consistently documented as a challenging process for inter professional healthcare teams, despite being a core tenet of interprofessional collaboration. This paper explores the role clarification process in two previously unexplored contexts: i) in the dissemination phase of a quality improvement (QI) program, and ii) as part of interorganizational partnerships for the care of chronic disease patients. METHODS: A secondary analysis using asynchronous purposive coding was conducted on an innovative pan-Canadian Chronic Obstructive Pulmonary Disease QI program. RESULTS: Our study reveals that the iterative structure of QI initiatives in the spread phase can offer numerous unique benefits to role clarification, with the potential challenge of time commitment. In addition, the role clarification process within interorganizational partnerships proved to be relatively well-structured, characterized by three phases: relationship conceptualization or early contact, familiarization, and finally, role division. Common strategies in the last stage included the establishment of working groups and new information-sharing networks. CONCLUSION: This article characterizes some ways in which providers and organizational partners negotiate their roles in a changing professional environment. As the movement towards integrated care continues, issues of role clarity are assuming increasing importance in healthcare contexts, and understanding role dynamics can provide valuable insight into the optimization of QI initiatives.
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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 | 0.048 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".