Do the Health of the Nation Outcome Scales Measure Outcome?
Bibliographic record
Abstract
OBJECTIVE: Among mental health outcome measures that have been developed for routine use, most of the information concerns the Health of the Nation Outcome Scales (HoNOS). This instrument is widely used in Europe, Australia, and New Zealand, but not in Canada. We tested its sensitivity and predictive validity under conditions that would resemble, as closely as possible, routine use. METHOD: Treating clinicians were asked to assess patients of all ages referred to outpatient mental health facilities of 2 district health authorities in Nova Scotia using either the HoNOS for adults or the HoNOS for Children and Adolescents (HoNOSCA). Data were entered using the existing routine administrative data system. RESULTS: We obtained at least 1 rating on 4620 patients, giving a completion rate of 82%. On follow-up, ratings for the global score and most of the individual items were sensitive to change (n = 808). After adjusting for confounders, a baseline HoNOS score was significantly associated with subsequent in and outpatient service use including admissions, bed days, and psychiatric contacts (n = 1359). CONCLUSIONS: HoNOS has satisfactory sensitivity and predictive validity for routine use. We could introduce the adult version and HoNOSCA simultaneously and collect data using routine databases. Given the widespread routine use of HoNOS internationally, using the same outcome measure in Canada would enable comparisons of illness severity and outcomes between jurisdictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".