Reliability and validity of the Japanese version of the Support Team Assessment Schedule (STAS-J)
Bibliographic record
Abstract
OBJECTIVE: The aim of this project was to develop an appropriate and valid instrument for assessment by medical professionals in Japanese palliative care settings. METHODS: We developed a Japanese version of the Support Team Assessment Schedule (STAS-J), using a back translation method, and tested its reliability and validity. In the reliability study, 16 nurses and a physician who work in a palliative care unit evaluated 10 hypothetical cases twice at 3-month intervals. For the validity study, external researchers interviewed 50 patients with matignancy and their families and compared the results with ratings by the nurses in the palliative care unit. RESULTS: Our results with hypothetical cases were: interrater reliability weighted kappa = 0.53-0.77 and intrarater reliability weighted kappa = 0.64-0.85. In the validity study comparing nurse evaluations and the results of interviews with patients and families, complete agreement was 36-70%, and close agreement (+/-1) was 74-100%. As a whole, weighted kappa were low: between -0.07 and 0.51. Our results were similar to those in the United Kingdom and Canada. SIGNIFICANCE OF RESULTS: Although this research was conducted under methodologically limited conditions, we concluded that the STAS-J is a reliable tool and its validity is acceptable. The STAS-J should become a valuable tool, not only for daily clinical use, but also for research.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".