Measurement Properties of Physical Therapy Patient Satisfaction Questionnaire (PTPSQ) in an Iranian Musculoskeletal Population
Bibliographic record
Abstract
Purpose: A valid and reliable tool that could measure patient satisfaction with physical therapy care for Persian-speaking patients will improve communication and enhance the involvement of people in research on health care quality and disparities. We aimed to evaluate the psychometric properties of the Persian version Physical Therapy Patient Satisfaction Questionnaire (PTPSQ). Methods: In this cross-sectional study, a prospective validation study design was adopted. In this methodological study, 297 patients from several physiotherapy centers in Kerman City, Iran, were evaluated using the PTPSQ. After the seventh session, a demographic questionnaire, visual analog scale, and the global rating of change were also answered by the participants (time point 1). The psychometric evaluation included factor analysis, divergent validity, convergent validity, and analysis of floor and ceiling effects. Reproducibility and internal consistency were investigated in this regard. To assess the test-retest reliability, 40 participants (randomly selected) completed the PTPSQ, again 24 to 48 hours later (time point 2). This research project was reviewed and approved by the Ethics Committee of the University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. SPSS v. 24 was used for statistical analysis. Results: The interclass correlation coefficient was in the range of 0.80-0.94 with the Cronbach alpha coefficient of 0.92. The standard error of measurement, minimal detectable change, and coefficient of variation for the questionnaire were 5.14, 14.39, and 0.21, respectively. Factor analysis revealed the 3-factor model. The relationship between the PTPSQ scores and the patient satisfaction index was relatively good (>0.40). Conclusion: Our results showed strong psychometric properties of the PTPSQ. Thus, we recommended its use in the Persian-speaking population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".