Selecting and training opinion leaders and best practice collaborators: experience from the Canadian Chiropractic Guideline Initiative.
Bibliographic record
Abstract
OBJECTIVES: To describe the process for selecting and training chiropractic opinion leaders (OLs) and best practice collaborators (BPCs) to increase the uptake of best practice. METHODS: In Phase 1, OLs were identified using a cross-sectional survey among Canadian chiropractic stakeholders. A 10-member committee ranked nominees. Top-ranked nominees were invited to a training workshop. In Phase 2, a national e-survey was administered to 7200 Canadian chiropractors to identify additional OLs and BPCs. Recommended names were screened by OLs and final selection made by consensus. Webinars were utilized to train BPCs to engage peers in best practices, and facilitate guideline dissemination. RESULTS: In Phase 1, 21 OLs were selected from 80 nominees. Sixteen attended a training workshop. In Phase 2, 486 chiropractors recommended 1126 potential BPCs, of which 133 were invited to participate and 112 accepted. CONCLUSIONS: OLs and BPCs were identified across Canada to enhance the uptake of research among chiropractors.
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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 | 0.100 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".