Using an Integrated Knowledge Translation (IKT) Approach to Enable Policy Change for Electronic Consultations in Canada
Bibliographic record
Abstract
This paper explores our efforts to support the expansion of a regional electronic consultation (eConsult) service on a national level by addressing potential policy barriers.We used an integrated knowledge translation (IKT) strategy based on five key activities leading to a National eConsult Policy Think Tank meeting: (1) identifying potential policy enablers and barriers; (2) engaging national and provincial/territorial partners; (3) including patient voices; (4) undertaking co-design and planning; and (5) adopting a solution-based approach.We successfully leveraged a diverse set of stakeholders in strategic discussions, culminating in actionable suggestions for next steps, which will serve to inform a national implementation strategy. RésuméCet article étudie les efforts déployés pour soutenir l' application à l'échelle nationale d' un service régional de consultation électronique (eConsultation), et ce, en abordant d'éventuels obstacles d' ordre politique.Nous avons employé une stratégie d' ACI fondée sur cinq activités clés qui ont nourri les discussions d' un groupe de réflexion national sur l' eConsultation : (1) repérer les obstacles et facteurs favorables d' ordre politique, (2) mobiliser les partenaires nationaux, provinciaux et territoriaux, (3) inclure le point de vue des patients, (4) s' engager dans la conception et la planification et (5) adopter une démarche axée sur les solutions.Nous avons réussi à impliquer un ensemble diversifié de partenaires dans les discussions stratégiques, ce qui a mené à la formulation de suggestions pratiques pour les prochaines étapes, lesquelles serviront à éclairer la stratégie de mise en œuvre nationale.T
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".