Becoming an Interprofessional Community of Practice: A Qualitative Study of an Interprofessional Fellowship
Bibliographic record
Abstract
Background: The social learning model, Communities of Practice (CoP), serves as an organizing framework for this study of interprofessional learning. The author, a nurse, completed the study while a doctoral student in a school of education. The objective of the study was to understand the phenomenon of participation in interprofessional learning experiences among a group of graduate students, faculty, and administrators, and the extent to which the markers of the communities of practice model were present in those experiences.Methods and Findings: This qualitative study used principles of constructivist grounded theory methodology. The objective was to seek out participants’ expressed experience as data to guide theory development. The participants were graduate students, faculty, and administrators from an interprofessional fellowship in developmental disabilities. Processes of building community and making meaning of the experience were themes that related to the Wenger CoP model. Feeling respected was a theme that was identified in this study and that is not found in the CoP model.Conclusions: The findings indicated that participants were able to form an interprofessional community of practice based on the markers of Wenger’s model. This initial study moves toward the development of an organizing theory of an effective interprofessional community of practice (EICoP).
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".