Collective leadership in collaborative practice: a qualitative secondary analysis of how plural leadership is enacted in practice
Bibliographic record
Abstract
, that occurs from the combined influence of individuals. We examined plural leadership in practice for the purpose of informing strategies to support leadership specifically, and interprofessional collaboration more generally. We conducted a secondary analysis of 13 semi-structured interviews collected as part of a larger study on interprofessional collaboration in long-term care. First, we categorized data using concepts theorized from research on plural leadership. Next, we identified themes within each of the categories that represented how plural leadership is enacted according to the concepts. We then combined these themes to arrive at three actionable ways that plural leadership is enacted in long-term care: familiarity to create leaders; sharing and empathy to foster leadership; and structuring leadership. We use these to offer some practical approaches based on evidence and theory to support plural leadership in practice. Strategies include supporting staff continuity, providing space to share knowledge, and equipping team members with tools for navigating organizational structures. Our work contributes theoretical ideas about how to study and support leadership in collaboration.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".