Clinical practice pattern of managing low back pain among physiotherapists in Bangladesh: A cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Low back pain (LBP) is the top global cause of disability, and physiotherapy interventions are used to manage it. However, understanding of the practice pattern of physiotherapists dealing with LBP patients in low- and middle-income countries (LMICs) is limited. This study aimed to explore the LBP practice pattern of LMIC’s (i. e., Bangladesh) physiotherapists by their demographic and professional factors. METHODS: This cross-sectional study sent a survey to randomly selected physiotherapists via email. RESULTS: Data of 423 illegible physiotherapist were analyzed. The majority of the physiotherapists (54.8%) were nongovernment service holders, and 87.7% worked in an urban setting. Recommended interventions were frequently used by only 12.3%, occasionally used by 66.2%, and 21.5% did not offer those interventions. Partially recommended interventions were frequently used by 33.3%, occasionally used by 43.7%, and never used by 23% of physiotherapists. For not recommended interventions, 69.3% occasionally, 13.5% frequently, and 17.3% never used such interventions. CONCLUSION: The study explored the practice pattern of physiotherapists of an LMIC by comparing available evidence-based practice guidelines for LBP. The findings of this study may provide an LMIC database to inform future research, clinical practice and education to ensure adherence to evidence-based LBP physiotherapy management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".