Psychometric properties of the SF‐36 in the early post‐stroke phase
Bibliographic record
Abstract
BACKGROUND: Patients who have had a stroke are a large group in whom long-term disability is common and therefore impaired general health is likely. The Short Form 36 (SF-36) is a popular measure of general health that has been used with this patient group, but not all aspects of its psychometric properties have been established for use in this context, and its use in the early post-stroke phase has been neglected. AIMS: To examine the reliability, validity and sensitivity to change of the SF-36 (UK version I) in patients in the early post-stroke period. DESIGN: A prospective, observational study of stroke outcomes was carried out. RESEARCH METHODS: From May 1996 to April 1997, patients who had had a stroke were identified by 24 general practices in Scotland and were recruited within 1 month of their stroke, whether in hospital or at home. Outcome measures including the SF-36 were administered at one, 3 and 6 months after onset. RESULTS: The internal consistency of the eight subscales at all three time-points was good except for 1 month Vitality (alpha = 0.6824) and 3 month General Health (alpha = 0.6650), which were borderline in comparison with the criterion value of 0.7. Construct validity was adequate overall, although correlations between Role Physical and General Health and the Barthel Index and Canadian Neurological Scale were lower than hypothesized. Most SF-36 subscales were sensitive to change between 1 and 3 months post-stroke, but none detected change between 3 and 6 months. CONCLUSIONS: There were some practical problems in using the SF-36 in an acutely unwell stroke population. However, analysis of psychometric properties suggested that most of the subscales were adequately reliable and valid. Sensitivity to change was poorer in the later stages of the study.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".