S82– Communities of practice and information technologies
Bibliographic record
Abstract
Guideline dissemination Other guideline dissemination Communities of practice (CoP) are interesting structures to facilitate intra- and interdisciplinary collaborations necessary to accelerate the implementation of best practices. In parallel, emergent web-based functionalities such as blogs, virtual libraries, and discussion forums can support CoP activities and thus enhance best-practices uptake. A mixed-methods approach was used. In-depth semi-structured interviews were conducted among rehabilitation health professionals engaged in an interdisciplinary and interorganizational stroke communities of practice. A literature review and a needs assessment was conducted to identify optimal web-based functionalities to be developed to support the CoP. Utilization of information technologies will be monitored. Content analysis of transcribed interviews reveals how underlying processes of trust-building, communication, and knowledge exchange improve problem solving at the systems level, leading to improved continuity of care for patients. Access to static information (virtual library) is perceived as a more useful functionality than discussion forums or blogs. Our study shows that information technologies are perceived as supportive but not necessary for knowledge exchange across health professionals. Communities of practice are effective means to accelerate knowledge exchange. Despite the availability of web-based application and innovative collaborative applications, health professionals highly value face-to-face meetings as a means to communicate and exchange on best practices.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".