Using Avoidable Admissions to Measure Quality of Care for Cardiometabolic and other Physical Comorbidities of Psychiatric Disorders: A Population-Based, Record-Linkage Analysis
Bibliographic record
Abstract
OBJECTIVE: Quality of care for comorbid physical disorders in psychiatric patients can be assessed by the number of avoidable admissions for ambulatory care sensitive (ACS) conditions. These are admissions for physical conditions that, with appropriate primary care, should not require inpatient treatment. Avoidable admissions for ACS conditions feature prominently in Australia's National Health Performance Framework and have been used to assess health care provision for marginalized groups, such as Indigenous patients or those of lower socioeconomic status. They have not been applied to people with mental illness. METHODS: A population-based, record-linkage analysis was used to measure ACS admissions for physical disorder in psychiatric patients of state-based facilities in Queensland, Australia, during 5 years. RESULTS: There were 77 435 males (48.0%) and 83 783 females (52%) (total n = 161 218). Among these, 13 219 psychiatric patients (8.2%) had at least 1 ACS admission, the most common being for diabetes (n = 6086) and angina (n = 2620). Age-standardized rates were double those of the general population. Within the psychiatric group, and after adjusting for confounders, those who had ever been psychiatric inpatients experienced the highest rates of ACS admissions, especially for diabetes. CONCLUSIONS: In common with other marginalized groups, psychiatric patients have increased ACS admissions. Therefore, this measure could be used as an indicator of difficulties in access to appropriate primary care in Canada, given the availability of similar administrative data.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".