Revamping the Day Hospital Program at North York General Hospital in response to COVID-related changes in patient demographics
Bibliographic record
Abstract
Abstract Background The Dynamic Sustainability Framework emphasizes the need for improving programs after implementation in response to the evolving environment. This report illustrates said framework and describes significant changes made to the Psychiatric Day Hospital (DH) at North York General Hospital (NYGH) in response to pandemic-related changes in participant demographic. Patient and staff satisfaction pre- and post-program modification are compared. Problem The COVID-19 pandemic resulted in increased DH referral acuity and patient affect dysregulation. The program needed to adapt to these changes and better serve the new DH patient population. Methods DH participants and team member feedback was gathered. Five major areas of improvement were identified. Changes were systematically introduced from July 2021 to January 2022. Feedback post-implementation in 2022-2023 from patients and DH team members were gathered for comparison. Interventions Dialectical Behavioral Therapy (DBT) was adopted as the theoretical basis of the revamped Day Hospital Program. All Day Hospital staff underwent training in DBT skills, with the creation of new treatment schedules and materials. Two separate streams were created for differing patient illness severity. The program continued to run during implementation of new changes, without disruption to the existing clinical workload. Results The program transitioned from a 3-week psychoeducational and rudimentary CBT program to a dual-stream 4-week DBT-based program to address patient acuity and higher prevalence of emotional dysregulation. Quantitative and qualitative feedback from new program participants have been positive. Conclusions The Day Hospital Program at NYGH made a successful transition in response to an evolving healthcare landscape.
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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 | 0.003 | 0.005 |
| 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 | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".