Factors Relating to the Expectations and Perceptions of Post-Stroke Outpatients’ in the Rehabilitation Services of Bangladesh
Bibliographic record
Abstract
Purpose: For enhancing patients’ participation, this study aims to identify the patient-related factors that relate to the expectations and perceptions of post-stroke outpatients’ in the rehabilitation services of Bangladesh. Methods: A questionnaire survey was administered to 342 outpatients from the stroke rehabilitation department. Descriptive statistical analysis was applied to measure patients’ perceptions and expectations. Eta statistic from ANOVA was applied to examine the relationship between patient-related factors and the patients’ expectations and perceptions of the rehabilitation services. Findings: Patients’ expectations rated higher than the perceptions in all the dimensions of rehabilitation services. Factors such as; (i) patients' education (0.222, p=0.005) and post-stroke disability (0.447, p<0.001) indicated a significant relationship with patients' expectations. (ii) Patients' education (0.210, p=0.010), occupation (0.226, p=0.003), family status (0.180, p=0.048) and daily activities before the stroke (0.169, p=0.044), post-stroke disability (0.195, p=0.004) and distance from home to the hospital (0.190, p=0.006) indicated a significant relationship with their perceptions in the rehabilitation services. Conclusion: The findings of this study concluded that the rehabilitation manager needs to work on these factors and recommended developing a continuing education program to minimize these factors of poor perceptions in the rehabilitation services.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".