Perceived determinants of clinical practice guideline implementation for stroke rehabilitation in LMICs a multinational REFORM survey
Bibliographic record
Abstract
Implementation of clinical practice guidelines (CPGs) is integral to improving the quality of stroke rehabilitation in low- and middle-income countries (LMICs). However, various barriers hinder their effective utilization. This survey aimed to identify the barriers faced by rehabilitation professionals in utilizing CPGs for post-stroke motor rehabilitation. A cross-sectional survey based on the Australian Living Guidelines for Stroke Rehabilitation was developed to identify factors that influence healthcare professionals' adherence to clinical practice guidelines. The survey comprised 50 questions spanning five domains: demographics, work practices, rehabilitation techniques, clinical practice awareness, and CPG feasibility and implementation. A panel of 10 experts validated the questionnaire. The survey was disseminated via emails, through professional associations, and platforms such as WhatsApp, LinkedIn, and X (formerly Twitter). Quantitative analysis data were analysed using Jamovi 2.3.21. The results indicated that less experienced professionals were more likely to implement CPGs, utilize telerehabilitation, and follow transition care protocols, while experienced practitioners adhered to both CPGs and hospital guidelines and employed motor outcome measures. Identified barriers included limited awareness, insufficient training, resource constraints, and challenges related to language and cultural relevance. To enhance CPG implementation, it is necessary to develop context-specific CPGs, establish stepwise clinical protocols, integrate evidence-based practice and CPG training into university curricula, and increase awareness among policymakers and stroke survivors. Engaging diverse stakeholders-patients, caregivers, multidisciplinary teams, and policymakers-is essential to foster an enabling environment for CPG adoption and advancing stroke rehabilitation practices in LMICs.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".