Perception towards Physiotherapy among the General Population in Gujrat, Pakistan
Bibliographic record
Abstract
INTRODUCTION: Physiotherapy is a dynamic profession that employs various therapeutic strategies to help people regain movement and function in their bodies. Physiotherapists provide comprehensive care for patients with different medical and surgical conditions. This study aimed to assess the perceptions regarding physiotherapy among the general population of Gujrat, Pakistan. METHODOLOGY: This descriptive cross-sectional survey was carried out among the general population of Gujrat from March to June 2021. Non-probability sampling technique was used to select the 126 participants >20 years of age. A self-structured Likert scale questionnaire was developed to collect data. The responses were analyzed through Statistical Packages for Social Sciences (SPSS). P-value less than 0.05 was considered as level of significance. RESULTS: Majority of participants (75.4%) were from the age group 20-29 years and 90(71.4%) of the participants had either undergraduate or postgraduation education. Out of 126 participants, 103(81.75%) participants were highly aware and had adequate awareness regarding physiotherapy, whereas 20(50.9%) participants showed moderate awareness and 3(2.4%) of participants showed a deficient level of awareness. No association was seen between awareness and variables including age, gender, socioeconomic status, and education. CONCLUSION: General population of Gujrat were highly aware of physiotherapy. Education plays an impactful role in better understanding of physiotherapy, so further steps should be taken to increase positive perception in different socioeconomic statuses, including advertisements, social media campaigns and seminars.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".