Electronic communities of practice: Guidelines from a project
Bibliographic record
Abstract
The timely incorporation of health research into the routine practice of individual health practitioners and interprofessional teams is a widely recognized and ongoing challenge. Health professional engagement and learning is an important cog in the wheel of knowledge translation; passive dissemination of evidence through journals and clinical practice guidelines is inadequate when used alone as an intervention to change the practices of the health professionals.An evolving body of research suggests that communities of practice can be effective in facilitating the uptake of best practices by individual health professionals and teams. Modern information technologies can extend the boundaries and reach of these communities, forming electronic communities of practice (eCoP) that can be used to promote intra- and interprofessional continuing professional development (CPD) and team-based, patient-centered care. However, examples of eCoPs and examination of their characteristics are lacking in the literature. In this paper, we discuss guidelines for developing eCoP. These guidelines will be helpful for others considering the use of the eCoP model in interprofessional learning and practice.
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 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 | 0.114 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.024 |
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".