Professional Early-Adopter Experiences of Implementing a Novel Rehabilitation Model of Care across Alberta, Canada: A Focused Ethnography
Bibliographic record
Abstract
Abstract Background In 2017, a provincial health-system released a Rehabilitation Model of Care (RMoC) to promote patient-centred care, provincial standardization, 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. Understanding RMoC implementation will expand implementation science knowledge, particularly around factors influencing model-of-care outcomes and sustainability in, and between, jurisdictions. We aimed to explore experiences of early-adopter providers and provincial consultants involved in the community-rehabilitation RMoC implementation in Alberta, Canada.Methods Via focused ethnography, we used focus groups (or interviews for feasibility/confidentiality) and aggregate, site-level data analysis of RMoC standardized metrics. Purposive sampling ensured representation across geography, service types and patient populations. Team-specific focus groups were onsite, at participants’ convenience, and led by a researcher-moderator and co-facilitator. A semi-structured question guide promoted discussions on interesting/challenging occurrences; perceptions of RMoC impact; and, suggested definitions of successful implementation. Focus groups and interviews were audio-recorded and transcribed alongside field notes. Data collection and analysis were concurrent to saturation. Transcripts were coded for implementation-related phrases. Similar ideas were collapsed forming themes, with inter-theme relationships identified. Tactics for rigour included negative case analysis, use of thick description, and an audit trail.Results We completed 11 focus groups and seven interviews (03/2018 to 01/2019) (n=45). Participants were 89.6% female, mostly-Canadian trained and represented diverse rehabilitation professions. Teams varied on their focal health service and patient population. The implementation experience involved navigating emotions, operating amongst 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 (e.g. 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 specific training priorities for future teams including evaluation training for novice teams; timelines for step-wise 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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".