Clinical practice pattern of low back pain among physiotherapists in a low-income country
Bibliographic record
Abstract
Background: Low back pain (LBP) is the top global cause of disability and physiotherapy interventions are used to manage it. However, the practice pattern of physiotherapists dealing with LBP patients in low-income countries are limited. Aim: The study aims to explore the LBP practice pattern of a low-income country’s ( i. e., Bangladeshi) physiotherapists by their demographic and professional factors. Methods: In a cross-sectional survey study, we have analyzed data from randomly selected 423 physiotherapists of Bangladesh who have invited to fill-up an online survey questionnaire about practice patterns. The first part of the questionnaire contained question demographic and professional background, second part included current intervention choices in the management of patients with LBP, the final part consisted of information on diagnosis, patient type and self-reported cure rate of LBP patients. Ethical approval: Clinical Trial Registry India: CTRI/2020/05/025313. Results: The Majority of the physiotherapists (54.8%) were non-government service holders and 87.7% worked in the town area. Regarding recommended interventions, only 12.3% frequently used those and 21.5% didn’t either offer or know about those interventions. For not recommended interventions, 69.3% occasionally, 13.5% frequently and 17.3% never used such interventions. The prevalence of good, moderate, and poor practice patterns was 14%, 62.4%, and 23.6% respectively. Participants‘ marital status (P = 0.003) and graduation institute category (P = 0.002) were significant factors for practice pattern variation. Conclusion: The study justified physiotherapy management status in a low-income country by comparing evidence-based practice guidelines. This finding set as a low-income country database to exhibit future research, clinical practice, and education for better LBP physiotherapy management adherence to evidence-based public health care.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".