Repeat Emergency Visits for Mental Health Patients: Before and during the Covid19 pandemic
Bibliographic record
Abstract
Introduction Frequent users of Emergency Departments (EDs) are a diverse group accounting for disproportionate EDs visits. Psychiatric patients are more likely to visit EDs (Slankamenac, 2020). EDs utilisation by psychiatric patients increased by 4.4% during COVID-19 pandemic. Objectives to determine frequent users characteristics within an Ottawa University Hospital, and assess Covid19 impact on overutilization of EDs compared to other hospitals. Methods Retrospective study of repeat visits characteristics, data extracted from EMR database. Repeat visits defined as no less than 30 days first visit to any EDs. Period of observation: March 1st, 2018 - February 28th, 2021 Results. Results 64% EDS visits for MH, 35% for addictions. More men (57%), age groups: 16-34 y.o. (41%), 34-64 y.o. (51%), 65 +y.o. (8%). Top presenting reasons: suicidality, self-harm, depression (40.5%). Anxiety, situational crisis (16%), bizarre behavior (12%). Most prevalent diagnoses: schizophrenia (28.7%), stress and anxiety (25.2%), personality disorders (13.5%) and depressive episode (10.6%). Only 35.1% admitted after repeat ED visits, 35.1% came by ambulance. Increase during peak pandemic exceeding 20%. Clearly pandemic created more pressures for MH services needs. Conclusions Schizophrenia and personality disorders made most prevalent diagnostic groups. Even when patients are in acute needs, they do not always require hospitalization. Investigating what MH conditions that got more stressed by the Covid19 pandemic will be of interest. Disclosure No significant relationships.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".