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. (English) [ABSTRACT FROM AUTHOR] \nObjectifs : Décrire le processus permettant de choisir et former les leaders d'opinion (LO) et collaborateurs des pratiques d'exemplaire (CPE) en chiropratique dans le but de favoriser l'adoption des pratiques d'excellence. Méthodologie : Lors de la première phase, on a désigné les LO au moyen d'une enquête transversale parmi les intervenants canadiens de la chiropratique. Un comité composé de dix membres a classé les candidats. Les candidats les mieux classés ont été invités à un atelier de formation. Lors de la deuxième phase, 7 200 chiropraticiens canadiens se sont soumis à une enquête nationale en ligne visant à désigner d'autres LO et CPE. Les noms recommandés ont été présélectionnés par les LO et le choix final s'est fait d'un commun accord. On s'est servi de webinaires pour former les LO à encourager leurs pairs à adopter des pratiques d'excellence et faciliter la diffusion des lignes directrices. Résultats : Lors de la première phase, on a choisi 21 LO parmi 80 candidats. Seize d'entre eux ont assisté à un atelier de formation. Lors de la deuxième phase, 486 chiropraticiens ont recommandé 1 126 LO potentiels, parmi lesquels 133 ont été invités à participer et 112 ont accepté. Conclusions : On a désigné des LO et CPE à l'échelle du pays pour favoriser l'adoption de la recherche parmi les chiropraticiens. (French) [ABSTRACT FROM AUTHOR] \nCopyright of Journal of the Canadian Chiropractic Association is the property of Canadian Chiropractic Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.
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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 | 0.075 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".