Assessing health-related quality of life in patients suffering from schizophrenia: a comparison of instruments
Bibliographic record
Abstract
OBJECTIVE: To compare three different kinds of health-related quality of life (HRQL) questionnaires available for use in patients suffering from schizophrenia: the SF-36 (a generic instrument), the QoLI (an instrument designed to a broad range of mental illnesses), the S-QoL (a questionnaire specific to schizophrenic patients), in terms of external validity and sensitivity to change. METHODS: Two hundred and five patients were included at D0 and one-third retested at D30. Socio-demographic data and clinical history were recorded, clinical evaluation comprised psychotic symptoms (PANSS), depression (Calgary depression scale for schizophrenia), global functioning (GAF), clinical severity (CGI), and extrapyramidal symptoms (ESRS). HRQL was assessed using the SF-36, the QoLI and the S-QoL. RESULTS: A better agreement is observed between the SF-36 and the S-QoL than between the QoLI and the two other instruments. S-QoL and SF-36 are more strongly correlated with clinical status than QoLI. Compared to the SF-36 and the QoLI, the S-QoL better discriminates patients with comorbidity from others. The S-QoL shows better responsiveness than the QoLI and the SF-36. CONCLUSION: For descriptive purpose, either generic tools like SF-36 or specific ones should be used, whereas when aiming at evaluating health treatment and care for schizophrenic patients, specific instruments like the S-QoL should be favoured.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".