Evidence-based stroke rehabilitation: do priorities for practice change and feasibility of implementation vary across high income, upper and lower-middle income countries?
Bibliographic record
Abstract
PURPOSE: The context of implementation plays an important role in the delivery of optimal treatments in stroke recovery and rehabilitation. Considering that stroke systems of care vary widely across the globe, the goal of the present paper is to compare healthcare providers' priority of key areas in translating stroke research to clinical practice among High Income Countries, Upper Middle- and Lower Middle-Income Countries (HICs, UMICs, LMICs). We also aimed to compare perceptions regarding the key areas' feasibility of implementation, and formulate recommendations specific to each socioeconomic region. METHODS: Data related to recommendations for knowledge translation in stroke, from a primary survey from the second Stroke Recovery and Rehabilitation Roundtable were segregated based on socioeconomic region. Frequency distribution was used to compare the key areas for practice change and examine the perceived feasibility of implementation of the same across HIC, UMIC and LMICs. RESULTS: A total of 632 responses from healthcare providers across 28 countries were received. Interdisciplinary care and access to services were high priorities across the three groups. Transitions in Care and Intensity of Practice were high priority areas in HICs, whereas Clinical Practice Guidelines were a high priority in LMICs. Interventions specific to clinical discipline, screening and assessment were among the most feasible areas in HICs, whereas Intensity of practice and Clinical Practice Guidelines were perceived as most feasible to implement in LMICs. CONCLUSION: We have identified healthcare providers' priorities for addressing international practice change across socioeconomic regions. By focusing on the most feasible key areas, we can aid the channeling of appropriate resources to bridge the disparities in stroke outcomes across HICs, UMICs and LMICs.IMPLICATIONS FOR REHABILITATIONIt is pertinent to examine the differences in priorities of stroke rehabilitation professionals and the feasibility of implementing evidence-based practice across socioeconomic regions.There is an urgent necessity for the development of clinical practice guidelines for stroke rehabilitation in Low-Middle Income Countries, taking into consideration the cultural, economic and geographical constraints.In upper-middle income countries, encouraging family support and timely screening and assessment for aphasia, cognition and depression appear to be the low hanging fruits to enhance quality of life after stroke.Innovative ways to increase intensity of practice and channelling of resources to improve transitions in care may prove to be the most beneficial in advancing stroke rehabilitation in high income countries.
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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 | 0.134 | 0.267 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".