Initiating communities of practice for teaching and education scholarship in hospital settings: a multi-site case study
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Background and Rationale: Numerous calls have been made for faculty development programming to better address faculty members' ongoing needs, to situate training strategies within the workplace and to utilize social learning perspectives, communities of practice in particular. Reviews have pointed to a paucity of published qualitative research on faculty development communities of practice and, more generally, on the processes of change and the organizational contexts in which interventions are implemented. Intervention: An initiative was started to instigate education scholarship communities of practice in three highly distinct academic health care settings, to address faculty members' ongoing needs for community and, ultimately, to serve as a source of support for the application of new knowledge to routine education activities. A research project was launched jointly to describe the process and progress of attempting to develop communities of practice at the three sites and to identify common and unique influences on sites' progress. Data Collection: Phone interviews were conducted with group facilitators from each site following group meetings, for the duration of the initiative. Analysis: Multiple case study methodology was employed to describe and compare the processes and progress of attempting to initiate communities of practice at the three sites and to identify obstacles related to organizational context. Findings: All three sites made limited progress in developing a shared domain of interest and a shared history of regular interaction (i.e. regular meetings). Participants identified different professional backgrounds and different education practices as challenges to establishing shared interest. More prominently, they identified busy schedules, geographic barriers, and absence of protected time as obstacles to regular and consistent meetings. Discussion: Difficulty establishing shared interest and shared history are considered in light of the unclear meaning of "education scholarship", cognitive and ethical boundaries between professions, and time constraints within modern, highly complex academic healthcare settings. Conclusions: While CoPs may appeal as self-sustaining, low-cost alternatives to formal programming, limited progress is possible without institutional investment and allowance commensurate with the implied scope and challenges.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| 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".