The anatomy of interprofessional leadership: An investigation of leadership behaviors in team‐based health care
Bibliographic record
Abstract
Abstract Increasing specialization among health care professions has heightened the need for proficient interprofessional teamwork. Within the team context for practice, leadership becomes a competency expected of all practitioners who must recognize the necessity of situational leadership dependent on patient needs and the professional competencies to meet those needs. Although this need for leadership within interprofessional practice is recognized, the behavioral components of that leadership competency have not been delineated. In this article, the authors report on a study to identify the behavioral components of interprofessional practice and highlight the indicators of leadership competency in interprofessional patient‐centered care. This qualitative study involved in‐depth interviews with 24 participants from nine professions engaged in collaborative team care of clients or patients in a variety of community and acute‐based health care facilities. Interprofessional competencies were explored using grounded theory, with coding of participants' responses. In this article, the authors have highlighted leadership in interprofessional practice, and discussed the behavioral indicators of leadership that could be used in preparation of students, faculty, and practitioners for interprofessional practice, as well as in evaluation of that practice for purposes of professional growth.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".