Geographic Clustering of Admissions to Inpatient Psychiatry among Adults with Cognitive Disorders in Ontario, Canada: Does Distance to Hospital Matter?
Bibliographic record
Abstract
Objective:This study examined relationships among hospital accessibility, socio-economic context, and geographic clustering of inpatient psychiatry admissions for adults with cognitive disorders in Ontario, Canada.Method:A retrospective cross-sectional analysis was conducted using admissions data from 71 hospitals with inpatient psychiatry beds in Ontario, Canada between 2011 and 2014. Data included 7,637 unique admissions for 4,550 adults with a DSM-IV diagnosis of Delirium, Dementia, Amnestic and other Cognitive Disorders. Bayesian spatial Poisson regression was employed to examine the relationship between accessibility of general hospitals with psychiatric beds and psychiatric hospitals, area-level marginalization, and hospitalization rate with the risk of admission to inpatient psychiatry among adults with cognitive disorders across 516 Forward Sortation Areas (FSA) in Ontario.Results:Residential instability and the overall hospitalization rate were significantly associated with an increase in the relative risk of admissions to inpatient psychiatry. Accessibility to general hospitals and psychiatric hospitals were marginally insignificant at the 95% credible interval in the final model. Significant geographic clustering of admissions was identified where individuals residing in FSA's with the highest relative risk were 2.0 to 7.1 times more likely to be admitted to inpatient psychiatry compared to the average.Conclusions:Geographic clustering of inpatient psychiatry admissions for adults with cognitive disorders exists across the Province of Ontario, Canada. At the geographic level, the risk of admission was positively associated with residential instability and the overall hospitalization rate, but not distance to the closest general or psychiatric hospital.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".