Rehospitalization risk factors for mental health and substance use in Northern British Columbia
Bibliographic record
Abstract
Mental health and substance use (MH&SU) rehospitalization rates are used as indicators of treatment quality, to reduce costs, and measure efficacy. Research on this topic in rural Canadian hospitals and communities is lacking. This study used secondary data on 5159 patients (age 15 and older) hospitalized with International Classification of Disease (ICD) F code MH&SU diagnosis. These patients had 9103 admissions to 18 hospitals in Northern British Columbia during a five-year period, April 1st, 2010 through March 31st, 2015. ANOVA and Tukey Post Hoc tests were used to examine associations of two performance measures with five patient factors; community size, Indigenous culture, relationship status, employment status, and ICD F code diagnoses. The first measure was number of hospital readmissions. Of the 5159 patients with 9103 admissions, 3482 (67.6%) had one hospital admission during the five-year period. The remaining 1677 (32.4%) patients had 3944 (43.3%) of the hospitalizations). Patients whose cultural identity was Indigenous had over-representation and increased readmissions. Patients who were single and never in a relationship had increased hospitalizations. Patients whose ICD F coding for schizophrenia or psychosis had increased hospitalizations. The second measure was wait time for community MH&SU follow-up. Of the 5159 patients, 4512 (87.5%) had contact with community MH&SU during the five-years. Urban communities with specialized MH&SU services had reduced wait times for follow up. Patients whose cultural identity was Indigenous had longer wait times for community MH&SU follow-up. Patients who were divorced or separated had longer wait times. Patients with ICD F coding for schizophrenia or psychosis had shorter wait times for follow-up. The relationship between hospital readmission and community MH&SU follow-up was examined using logistic regression with the five factors. An inverse relationship was found between the two performance measures. Patients who did not have community MH&SU follow-up within 30 days had reduced odds ratio of readmissions, whereas patients who had follow-up within 30 days had increased odds ratio for readmissions. Although the study finds support for patient risk factors, evidence suggests approaches like a Decision Support Tool (DST) might provide reliability for intervention, and resource planning, as well as timely intervention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".