Implementation of a novel rehabilitation model of care across Alberta, Canada: a focused ethnography
Bibliographic record
Abstract
BACKGROUND: In 2017, a provincial health-system released a Rehabilitation Model of Care (RMoC) to promote patient-centred care, provincial standardisation and data-driven innovation. Eighteen early-adopter community-rehabilitation teams implemented the RMoC using a 1.5-year-long Innovation Learning Collaborative (in-person learning sessions; balanced scorecards). More research is required on developing, implementing and evaluating models of care. We aimed to explore experiences of early-adopter providers and provincial consultants involved in the community-rehabilitation RMoC implementation in Alberta, Canada. METHODS: Using focused ethnography, we used focus groups (or interviews for feasibility/confidentiality) and aggregate, site-level data analysis of RMoC standardised metrics. Purposive sampling ensured representation across geography, service types and patient populations. Team-specific focus groups were onsite and led by a researcher-moderator and cofacilitator. A semistructured question guide promoted discussions on interesting/challenging occurrences; perceptions of RMoC impact and perceptions of successful implementation. Focus groups and interviews were audio-recorded and transcribed alongside field notes. Data collection and analysis were concurrent to saturation. Transcripts coding involves collapsing similar ideas into themes, with intertheme relationships identified. Rigour tactics included negative case analysis, thick description and audit trail. RESULTS: We completed 11 focus groups and seven interviews (03/2018 to 01/2019) (n=45). Participants were 89.6% women, mostly Canadian trained and represented diverse rehabilitation professions. The implementation experience involved navigating emotions, operating among dynamics and integrating the RMoC details. Confident, satisfied early-adopter teams demonstrated traits including strong coping strategies; management support and being opportunistic and candid about failure. Teams faced common challenges (eg, emotions of change; delayed data access and lack of efficient, memorable communication across team and site). Implementation success targeted patient, team and system levels. CONCLUSIONS: We recommend training priorities for future teams including evaluation training for novice teams; timelines for stepwise implementation; on-site, in-person time with a facilitator and full-team present and prolonged facilitated introductions between similar teams for long-term mentorship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".