Evaluating the impact of a champion on implementation of the Back Skills Training (BeST) programme in Canada: A mixed methods feasibility study protocol
Bibliographic record
Abstract
Introduction \n \nThere is global recognition that low back pain (LBP) should be managed with a biopsychosocial approach. Previous implementation of this approach resulted in low uptake and highlighted the need for ongoing support. This study aims to explore the feasibility of (i) training and using a champion to support implementation, (ii) using a cluster randomised controlled trial (RCT), (iii) collecting patient reported outcome measures in a Canadian public healthcare setting and to identify contextual barriers to implementation. \n \nMethods \n \nA pragmatic cluster RCT with embedded qualitative study with physiotherapists treating LBP in publicly funded physiotherapy departments in Newfoundland and Labrador, Canada. Participants will complete a previously developed online training course to equip them to deliver a biopsychosocial intervention for LBP. Clusters randomised to the intervention arm will receive additional support from a champion. A minimum champion training package has been developed based on known barriers in the literature. This includes strategies to target barriers relating to group-based scheduling issues, lack of managerial support, perceived patient factors such as addressing patient expectations for other types of treatments or selecting which patients might be best suited for this intervention, and anxiety about delivering something new. This package will be further codeveloped with study champions based on identified implementation barriers using the Behaviour Change Wheel. Clusters will be monitored for 6 months to assess champion and physiotherapist recruitment and retention, acceptability and implementation of the champion training, and the viability of conducting a cluster RCT in this setting. A purposive sample of physiotherapists will be interviewed from both arms. \n \nEthics and dissemination \n \nThis study was approved by Newfoundland and Labrador Health Research Ethics Authority in December 2018. Results will be disseminated to academic audiences through conferences and peer reviewed publications; to all study participants, their clinical leads, and patients with LBP. \n \nTrial registration number \n \nClinicalTrials.gov Identifier: NCT04377529; Memorial University of Newfoundland Protocol Record 20190025; Pre-results.
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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 | 0.053 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".