Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recent literature has been critical of research that adopts a narrow focus on single leaders and on leadership attributes and has called for attention to leadership that is distributed among individuals and to practices in which leaders engage. We conducted a study of health care teams where we attended to role distribution among leadership constellation members and to loose or tight coupling practices between leaders and the remainder of the team. This focus provides insights into how leadership can be practiced and structured to enhance team functioning. METHODOLOGY: A qualitative, multicase study of four teams was conducted. Data collection involved 44 interviews with almost all the members of the teams and 18 team meeting observations. Thematic analysis was conducted by the two authors. FINDINGS: Leadership constellations can give rise to leadership role overlaps and gaps that may create ambiguity within teams, ambiguity is diminished if the leaders can agree on which leader assumes ultimate authority in an area, the presence of more leaders does not necessarily entail more comprehensive fulfillment of team needs, and teams' needs for tight or loose leadership practices are influenced by contextual factors that we elaborate. PRACTICE IMPLICATIONS: (a) It is important to recognize areas of overlap and gaps in leadership roles and to provide clarity about role boundaries to avoid ambiguity. Role mapping exercises and open discussions should be considered. (b) Attempting to spread formal leadership responsibilities informally among individuals is not always a workable strategy for addressing team needs.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".