Providing crisis care in a pandemic: a virtual based crisis stabilization unit
Bibliographic record
Abstract
Background Winnipeg, Canada operates a 16-bed subacute unit, the Crisis Stabilization Unit (CSU), for voluntary patients in crisis not requiring hospital admission. The virtual CSU (vCSU) launched in March 2020 as an adjunct to the in-person CSU during the COVID-19 pandemic, providing the same resources virtually, allowing patients to remain at home. Methods Program data were collected for vCSU admissions between April 1, 2020 and April 7, 2021 (n = 266) to examine patient characteristics and discharge outcomes. Data were retrieved from the electronic patient record (EPR) for both in-person and vCSU admissions during the same period for comparison (n = 712). vCSU admissions (n = 191) were summarized by patient demographics, clinical factors/outcomes, and compared on the same measures to in-person CSU admissions (n = 521) using binary logistic regression. Results 30.1% of patients admitted to the vCSU received initial mental health assessment virtually (phone/videoconference), therefore receiving all care at home. Clinical symptoms at assessment included depression/anxiety (39.0%), psychosis/mania (2.7%), suicidal behaviour/self-harm (27.4%), psychosocial event/stressor (19.8%). Average stay was 4.9 days. Compared to the in-person CSU, vCSU referrals were associated with the absence of psychosis [odds ratio (OR).40, 95% confidence interval (CI).18–0.89] and no prior 1-year contact with referral site (OR.43, 95% CI.28–0.64). Those living farther away from the referral site were more likely to receive a vCSU referral. Conclusion The vCSU model is feasible for a diverse group of patients experiencing mental health crises. Future work is needed to better determine who the model is right for and examine longer term outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".