Experiences of shared decision-making in community rehabilitation: a focused ethnography
Bibliographic record
Abstract
BACKGROUND: Shared decision-making (SDM) can advance patient satisfaction, understanding, goal fulfilment, and patient-reported outcomes. We lack clarity on whether this physician-focused literature applies to community rehabilitation, and on the integration of SDM policies in healthcare settings. We aimed to understand patient and provider perceptions of shared decision-making (SDM) in community rehabilitation, particularly the barriers and facilitators to SDM. METHODS: We used a focused ethnography involving 14 community rehabilitation sites across Alberta, including rural, regional-urban and metropolitan-urban sites. We conducted semi-structured interviews that asked participants about their positive and negative communication experiences (n = 23 patients; n = 26 providers). RESULTS: We found SDM experiences fluctuated between extremes: Getting Patient Buy-In and Aligning Expectations. The former is provider-driven, prescriptive and less flexible; the latter is collaborative, inquisitive and empowering. In Aligning Expectations, patients and providers express humility and openness, communicate in the language of ask and listen, and view education as empowering. Patients and providers described barriers and facilitators to SDM in community rehabilitation. Facilitators included geography influencing context and connections; consistent, patient-specific messaging; patient lifestyle, capacity and perceived outlook; provider confidence, experience and perceived independence; provider training; and perceptions of more time (and control over time) for appointments. SDM barriers included lack of privacy; waitlists and financial barriers to access; provider approach; how choices are framed; and, patient's perceived assertiveness, lack of capacity, and level of deference. CONCLUSIONS: We have found both excellent experiences and areas for improvement for applying SDM in community rehabilitation. We proffer recommendations to advance high-quality SDM in community rehabilitation based on promoting facilitators and overcoming barriers. This research will support the spread, scale and evaluation of a new Model of Care in rehabilitation by the provincial health system, which aimed to promote patient-centred care.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".