Structural Validity and Reliability of a Tool for Clinical Rehabilitation Staff to Evaluate Life-Goal-Setting Practice for Cancer Survivors
Bibliographic record
Abstract
Background: There is a need for an assessment tool for clinical rehabilitation staff to evaluate their life-goal-setting practice, especially in oncology rehabilitation. This study aimed to confirm the structural validity and reliability of the 21-item Reengagement life Goal Assessment Tool for Cancer survivors (ReGAT-C) with a five-category response scale. Methods: Participants were clinical rehabilitation staff who worked at designated cancer care hospitals in Japan and had experience in setting life-goals with cancer survivors hospitalized during the non-terminal phase. The ReGAT-C was mailed to participants twice, and Rasch analysis was repeated on the scores of the first ReGAT-C to test structural validity and reliability. The test–retest reliability was also examined using the scores of the first and second ReGAT-Cs after revising it according to the Rasch analysis results. Results: A total of 121 participants completed the first ReGAT-C, and 70 participants completed the second ReGAT-C. Following three Rasch analyses, the ReGAT-C was revised to contain 14 items with a three-category response scale. The revised scale showed satisfactory psychometric properties. Conclusions: The 14-item ReGAT-C with a three-category response scale could help staff to identify elements that are lacking in their practice and adjust their policies based on the items’ difficulty.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".