Virtual knowledge production within a physician educational outreach program
Bibliographic record
Abstract
This paper describe the impacts and lessons learned of using conferencing technologies to support knowledge production activities within an academic detailing group. A three year case study was conducted in which 20 Canadian health professionals collaborated on developing educational outreach materials for family physicians. The groups communicated in face-to-face, teleconferencing, and web-conferencing environments. Data was collected over three years (2004-2007) and consisted of structured interviews, meeting transcripts, and observation notes. The analysis consisted of detailed reviews and comparisons of the data from the various sources. The results revealed several key findings on the on the impacts of conferencing technologies on knowledge production activities of academic detailers. The study found that: 1) The rigid communication structures of web-conferencing forced group members to introduce other tools for communication 2) Group discussions were perceived to be more conducive in face-to-face meetings and least conducive teleconferencing meetings; 3) Web-conferencing had an impact on information sharing; 4) Web-conferencing forces group interaction “within the text”. The study demonstrates the impacts and lessons learned of academic detailing groups collaborating at a distance to produce physician education materials. The results can be used as the bases for future research and as a practical guide for collaborative academic detailing groups working within a virtual collaborative and educational environment.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".