Conformance to Evidence-Based Treatment Recommendations in Schizophrenia Treatment Services
Bibliographic record
Abstract
OBJECTIVE: To assess quality of health care provided in a representative Canadian mental health service using conformance to evidence-based treatment recommendations, and to examine differences from published US results. METHOD: We used a cross-sectional cohort design involving a randomly selected sample of patients diagnosed with schizophrenia attending 1 of 3 mental health clinics in 1 Canadian regional health system. The sample size was calculated to detect differences with the US sample. Conformance criteria were based on a published protocol. Data were collected using patient interviews and a structured review of health records. Conformance to 9 key Schizophrenia Patient Outcomes Research Team recommendations was assessed. RESULTS: Conformance ranged between 58% and 90% for pharmacological recommendations, and 0% to 81% for psychosocial recommendations. No patients who met criteria for assertive case management had been referred to an assertive case management team. Significant differences in conformance rates to some treatment recommendations were found between Canadian and published US results. CONCLUSIONS: It proved possible to assess health care quality using process measures of conformance to treatment recommendations. Conformance to clinical recommendations for pharmacotherapy is higher than for psychosocial therapies. The absence of barriers to access for pharmacological therapies likely enhances the higher conformance to these recommendations. Limited or variable access to psychosocial services, specifically assertive community treatment, likely negatively affects conformance to psychosocial treatment recommendations. Methodological limitations preclude drawing conclusions on comparisons between Canadian and US services.
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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 | 0.093 | 0.356 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".