Validation of a translated Chinese version of the Participation Strategies Self-Efficacy Scale: a cross-sectional study
Bibliographic record
Abstract
Purpose To examine the psychometric properties of a Chinese version of the Participation Strategies Self-Efficacy Scale (PS-SES) among stroke survivors.Methods The PS-SES was translated into Chinese. A cross-sectional descriptive study was conducted with 336 stroke survivors recruited from the neurology departments of five hospitals in China. Reliability, concurrent validity, and construct validity of the scale were determined.Results The Chinese version of the PS-SES (PS-SES-C) showed good internal consistency and test–retest reliability, with a Cronbach’s α of 0.98 and an intraclass correlation coefficient of 0.79. There was a moderate to strong positive correlation between the PS-SES-C and Chinese version of the General Self-Efficacy Scale (r = 0.59, p < .001), positive correlations between the PS-SES-C and Chinese versions of the Modified Barthel Index (r = 0.59, p < .001), Rivermead Mobility Index (r = 0.70, p < .001), and Reintegration to Normal Living Index (r = 0.70, p < .001), and a negative correlation between the PS-SES-C and National Institutes of Health Stroke Scale (r = −0.63, p < .001). Known-group validity and factorial validity were also supported.Conclusions The PS-SES-C is a reliable and valid instrument for assessing self-efficacy in managing the participation of Chinese stroke survivors.Implications for rehabilitationSelf-efficacy significantly predicts activity and participation in stroke survivors and is a major outcome measure in many stroke rehabilitation programmes.The translated Chinese version of the Participation Strategies Self-efficacy Scale is a valid and reliable tool to evaluate stroke survivors’ self-efficacy in managing participation.The Chinese version of the Participation Strategies Self-efficacy Scale can be used to assess stroke recovery among the Chinese population in clinical and research settings.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".