Responsiveness of the SF-36 Questionnaire to the Treatment Outcome: A Comparison of the Mental and the Physical Patients
Bibliographic record
Abstract
Background: The main aim of the current paper was to study the level of the SF-36 sensitivity to the treatment outcome between mental and physical patients.Method: A random sample of 40 physical patients (Heart, Pulmonary, Kidney, Diabetes, and Rheumatism) as well as 40 mental patients (Depression, Obsession, Schizophrenia, and Psychosis) were asked to fill out the SF-36 questionnaire three times at intervals of 10-15 days.The independent as well as paired sample t-test statistic was used to analysis data. All analyses were conducted using SPSS statistical software (version 17.0; SPSS).Results: Our findings indicated that there is a significant increase in mean score of different domain of SF'36 questionnaire, for both physical and mental patients, as they showed an improvement on their current health status. In sum, our results can provide more psychometric evidence for sf-36 sensitivity to treatment process in an Iranian sample of physical and mental patients.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".