Leading team learning: what makes interprofessional teams learn to work well?
Bibliographic record
Abstract
This article describes an ethnographic study focused on exploring leaders of team learning in well-established nephrology teams in an academic healthcare organization in Canada. Employing situational theory of leadership, the article provides details on how well established team members advance as "learning leaders". Data were gathered by ethnographic methods over a 9-month period with the members of two nephrology teams. These learning to care for the sick teams involved over 30 regulated health professionals, such as physicians, nurses, social workers, pharmacists, dietitians and other healthcare practitioners, staff, students and trainees, all of whom were collectively managing obstacles and coordinating efforts. Analysis involved an inductive thematic analysis of observations, reflections, and interview transcripts. The study indicated how well established members progress as team-learning leaders, and how they adapt to an interprofessional culture through the activities they employ to enable day-to-day learning. The article uses situational theory of leadership to generate a detailed illumination of the nature of leaders' interactions within an interprofessional context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".