Translation and validation of the Bahasa Malaysia version of the Patient-Specific Functional Scale (PSFS)/ Akehsan Dahlan, Muhammad Danial Mohd Yasin, Zati Izni Achmy
Bibliographic record
Abstract
Purpose This study aimed to translate and culturally validate the Patient-Specific Functional Scale (PSFS) into the Bahasa Malaysia version to the elderly with sarcopenia. Methods The PSFS underwent an established process of translation and cross-cultural adaptation from a published guideline. A total of 94 community living elderly participated in this study. The investigated psychometric properties of the PSFS-BM included concurrent validity, convergent validity, and test-retest reliability. The elderly completed the English version of PSFS and the PSFS-BM for the evaluation of the concurrent validity. The convergent validity was assessed by comparing the PSFS-BM with the Canadian Occupational Performance Measure (COPM). A two weeks interval was set to assess test-retest reliability. Results Pearson correlation coefficient of 0.97 indicated that a strong positive correlation exists between the English version of PSFS and the PSFS-BM. The Pearson correlation coefficient with the r value of 0.78 and 0.69 also reflect a strong relationship of the PSFS-BM with the COPM. The test-retest reliability was good with the intraclass correlation coefficient of 0.84. The standard error of measurement and the minimal detectable change was 0.6 and 1.4, respectively. The Bland-Altman plot indicated a good agreement between the initial test and retest scores. A strong correlation of the initial test and retest scores was also observed using the Pearson correlation coefficient. Conclusion The PSFS-BM is a valid and reliable instrument to assess the functional ability of the community living elderly with the possibilities of sarcopenia.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".