The role of a remote knowledge broker from an academic setting using an adaptive approach to implement evidence-based practice in primary care settings: A case study of integrating mood management interventions for treatment seeking tobacco users.
Bibliographic record
Abstract
Abstract Background: Knowledge brokering is an emerging knowledge translation strategy used within healthcare to bridge the gap between evidence and practice. Reported studies indicate that the day-to-day role of a knowledge broker often involves in-person communication with frontline workers and decision makers. However, travelling to primary care sites can be cost- and resource-intensive and thus not feasible. In this paper, we describe the role and experience of a remote knowledge broker (rKB) working in an academic health sciences centre, delivering tailored one-on-one support to end-users using phone and email communications. Methods: A rKB was hired to support (n = 62) English-speaking Family Health Teams (FHTs) across Ontario with implementing mood management interventions as part of an existing smoking cessation program, the Smoking Treatment for Ontario Patients (STOP) program. We describe the eight categories of tasks performed by the rKB over a 12-month period, as well as their experience communicating via technology to develop relationships with healthcare providers (HCPs). Results: Sixty-one of the 62 FHTs (n = 73 HCPs) were provided rKB services. The total number of successful phone and email communications with the rKB ranged from 3-98 interactions over 12 months. Common barriers to implementation reported by FHTs were associated with the Inner and Outer Setting domains of the Consolidated Framework for Implementation Research (CFIR) and included lack of time, resources, and patient engagement. Conclusions: The role of the rKB involved building relationships with HCPs, identifying and helping to problem solve barriers, and building capacity in the field. Similar to traditional knowledge brokering, this analysis shows that developing a meaningful relationship between a remotely situated KB and HCPs could take anywhere between 1-6 months. Using implementation frameworks such as CFIR can help the rKB identify barriers and be ready to address them. In addition, hiring a rKB with previous engagements and knowledge of the local context may facilitate clinical practice change. Our future work will evaluate the cost-effectiveness of rKBs to inform its potential to be scaled up.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".