Sixteen Months “From Square One”: The Process of Forming an Interprofessional Clinical Teaching Team
Bibliographic record
Abstract
Background: Descriptions of interprofessional education (IPE) programs and teacher competencies exist, but limited research has been undertaken about the process of IPE teaching team formation. This research project examined how pedagogically naïve clinicians of different disciplines initially formed an IPE teaching team.Methods and Findings: A case study approach was undertaken with data collected over the first sixteen months of an IPE program. Data included: audio recordings, transcripts, and field notes from nine individual teacher interviews, two teaching team focus groups, five student focus groups, and eight summary reports. Data analysis using a grounded theory constant comparison approach revealed themes relating to the formation, development, and evolving sophistication of the teaching team from functioning, to co-ordinating, to co-operating, and finally to collaborating. These stages were influenced by four external factors: remote rural context, Hauora Māori principles, personal attributes, and teacher development.Conclusions: Formation of interprofessional clinical teaching teams requires educational preparation, time learning to work with each other, and trust development, with a number of local contextual factors influencing this process. Teaching team formation paralleled Wegner’s Community of Practice model where shared vision supported the adoption of an increasingly complex IPE pedagogy.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".