Developing a Virtual Interdisciplinary Research Community in Clinical Education: Enticing People to the “Tea-Room”
Bibliographic record
Abstract
Background: Many interdisciplinary collaborative research programs in the health sector are adopting the community of practice concept within virtual environments. This study explores the factors that affect the members of a geographically dispersed group of health professionals in their attempt to create an interprofessional Virtual Community of Practice (VCoP) from which to promote clinical education research.Method & Findings: A survey was used to determine participants’ degree of computer competency. System logs recorded members’ access details and site activity. Member perceptions and beliefs were established using focus groups. While members stated they were enthusiastic about the VCoP, the primary use was viewing. Their online behaviour indicated that on average it took six visits to generate a post. This suggests a stronger focus on viewing (consumption of) information than on contributing (construction of) information.Conclusions: We believe it is crucial for members to contribute during the initial phase of any pre-structured VCoP in order to overcome the consumption-construction dilemma. It is during this initial phase that members will decide on the community’s value. If the community cannot offer added value, members who engage are likely to consume for a time and then leave.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".