The role of Psychiatric-physical multimorbidity and continuity of care in seniors’ medical emergency visits
Bibliographic record
Abstract
Multimorbidity contributes to complexity in seniors, but the impact of co-occurring physical and psychiatric illnesses on emergency department (ED) visits has received little attention. We investigated relationships between trans-diagnostic psychiatric severity, physical multimorbidity, and their interaction with non-psychiatric ED use; and tested the association of continuity of primary care on these relationships. A retrospective cohort design (n = 2,560,986) measured exposures to physical multimorbidity, psychiatric severity, and continuity in primary care. The main outcome was number of medical ED visits. At each level of physical multimorbidity, non-psychiatric ED visits increased with psychiatric severity. There were direct effects of physical multimorbidity (OR 1.35, 95%CI 1.35 - 1.35), psychiatric severity (OR 1.52, 95%CI 1.49 - 1.54), and continuity of care (low vs high OR 1.26, 95%CI 1.24 - 1.28) on frequent non-psychiatric ED use. Continuity of care did not mediate the relationships of physical multimorbidity, psychiatric severity or their interaction on frequent non-medical ED use. Transdiagnostic psychiatric severity correlates with seniors using the ED for non-psychiatric reasons, especially for repeated visits, in addition to the expected contribution of physical multimorbidity. Continuity of primary care does not mediate this relationship. Understanding the contribution of regular primary care requires further investigation.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".