Relationship Between Kinesiophobia and Fear of Falling in Patients Suffering from Stroke Leading to Physical Disability in Selected Rehabilitation Center of Bangladesh
Bibliographic record
Abstract
Abstract Background Psychological barriers in stroke rehabilitation remain understudied in low-resource settings. This cross-sectional study examines relationships between kinesiophobia, fear of falling (FOF), and physical disability in Bangladeshi stroke patients. Methods Using validated Bangla versions of WHODAS 2.0 (12-item), Tampa Scale for Kinesiophobia (TSK-17), and Falls Efficacy Scale-International (FES-I), we assessed 200 patients from two rehabilitation centers. Pearson correlations and linear regression analyzed associations between psychological factors and disability. Results Participants showed moderate-severe scores: WHODAS 2.0 (M = 43.65 ±6.78), TSK (M = 47.26 ±5.44), FES-I (M = 49.12 ±7.20). Strong correlations emerged between WHODAS-FES-I (r = 0.65, 95% CI: 0.58–0.71) and TSK-FES-I (r = 0.55, 95% CI: 0.47–0.62). Regression models identified age ≥56 (β = 0.34, p = 0.002) and female gender (β = 0.28, p = 0.008) as significant predictors of higher psychological scores. Conclusion Psychological factors strongly correlate with physical disability in Bangladeshi stroke survivors, with demographic predictors suggesting the need for gender- and age-specific interventions. Study limitations include recruitment from two urban centers and a cross-sectional design. Integration of psychological assessment in rehabilitation protocols is recommended.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".