Facilitators and barriers to patient-centred goal-setting in rehabilitation: A scoping review
Bibliographic record
Abstract
OBJECTIVE: Identify, map, and synthesize existing reviews, to extract and analyse the most prominent barriers and facilitators to applying patient-centred goal-setting practice in rehabilitation using the Capability, Opportunity Motivation Behaviour (COM-B) model. DESIGN: Scoping review. DATA SOURCE: A primary search was conducted in MEDLINE, CINAHL, EMBASE, PsychInfo, and Cochrane. Citation chaining was employed. REVIEW METHODS: All types of review (systematic, scoping, and narrative) studies published up to June 14, 2022 that included physical and neurological rehabilitation, patient-centeredness, and goal-setting were reviewed. Studies were scrutinized for relevance, quality was not assessed. The most prominent barriers and facilitators were synthesized using thematic content analysis and mapped onto the COM-B model. RESULTS: Twenty-six review studies covering a range of conditions and settings, acute to community were included. Barrier and facilitators were identified at patient, provider, and organizational level. Barrier themes include provider's existing beliefs about goal-setting, lack of skills, and integration into clinical routines. Patient barriers related to capacity and opportunity to participate. Organizational barriers include lack of clinical guidelines, patient preparation, insufficient provider time, and high productivity expectations. Facilitators included goal-setting guidelines, training and education of providers and patients, revised clinical routines, performance monitoring, adequate time, and resources. CONCLUSION: Healthcare providers should be the primary target of intervention. A provider's motivation to change current practice is the most prominent barrier, followed closely by capacity and opportunity. Patients require information, training, and structured engagement opportunities. Organizations play a key role in creating the optimal environmental conditions to enable patient-centred goal-setting.
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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 | 0.085 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".