PROVIDING CRISIS CARE IN A PANDEMIC – A VIRTUAL-BASED CRISIS STABILIZATION UNIT
Bibliographic record
Abstract
Introduction: The goal of this study was to describe the virtual Crisis Stabilization Unit (vCSU), present patient demographics and outcomes, comment on the feasibility and effectiveness of virtual models of care, and explore physician assistant (PA) roles in Psychiatry and their views on the provision of virtual crisis care. Methods: Winnipeg operates a 16-bed stepped-care unit (the Crisis Stabilization Unit; CSU) for voluntary patients in crisis that do not require admission to hospital. The vCSU was developed in March 2020 as an adjunct to the in-person CSU for safety reasons in the COVID-19 pandemic. Individuals in the vCSU had access to all of the same resources as those admitted to the in-person CSU unit (coping skills-based classes, medication reminders, 24/7 access to crisis support). Patient demographics, presenting complaints, and outcome data were collected through discharge surveys for individuals admitted to the vCSU during a 6-month period. Additionally, anonymous online surveys were sent to practicing PAs at the CRC to explore their scope of practice and views on virtual care. Results: From March to August 2020 a total of 95 patients were admitted to the vCSU. Of these, thirty-one individuals (32.6%) had their vCSU intake either by phone or videoconference, therefore remaining at home for the entirety of their care. Seventy-six (39.2%) individuals had suicidal/self-harm behaviour at presentation, twenty-one (22.7%) reported depression and anxiety, seven (3.6%) had symptoms of mania or psychosis. Six individuals (6.6%) required psychiatric involvement; no individuals were admitted to hospital. PAs have a wide scope of practice in Psychiatric care from conducting psychiatric consultation to facilitating discharge planning. PAs agree that the vCSU improves accessibility to crisis care and have identified connection issues as the main barrier to virtual psychiatric care. Conclusion: A virtual crisis ward is a feasible and effective mechanism for providing access to care and connection to resources for patients experiencing crisis and PAs can play a key role in the provision of crisis care.
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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 | 0.001 | 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) | 0.007 | 0.001 |
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".