Validation de la version française du Health of the Nation Outcome Scales (HoNOS-F)
Bibliographic record
Abstract
OBJECTIVE: This study reports the validation of the French version of the Health of the Nation Outcome Scales (HoNOS-F), a questionnaire developed to measure health and social functioning of people with mental illness. METHOD: Once each statement was tested for readability, the scale was administered to 3 samples of people suffering from severe mental disorders to estimate its reliability and validity. More specifically, tests were run to establish the internal consistency, the stability, and the interrater reliability of the HoNOS-F. Confirmative factor analyses and mean differences according to age, sex, and diagnosis were also conducted to evaluate respectively construct- and criterion-related validity. RESULTS: Coefficients obtained from the various tests show that the scale is reliable only when the total score is used. The confirmatory factor analyses indicate that the observed data do not fit the 2 proposed models, a unidimensional model and a 4-dimension model. However, the scale did show criterion-related validity. CONCLUSIONS: Results of the present study converge with those obtained on the original widely used English version. Therefore, we suggest that clinicians use the questionnaire by referring to each item separately and by considering such patient characteristics as age, sex, and diagnosis. We also suggest that researchers wishing to evaluate health and social functioning of persons with serious mental disorders use the total score. Caution is, however, warranted when interpreting the total score for a French-speaking population, because the factorial solution 1-dimension model did not prove to be satisfactory.
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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 | 0.045 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".