Trends in visits to a 24-hour walk-in crisis mental health centre during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction The sudden onset of the COVID-19 pandemic in the spring of 2020 introduced new stressors and exacerbated existing ones, which for many negatively impacted mental health or aggravated prior mental illness. As such, access to crisis care services was necessary and potentially increased, alongside public fears about virus contagion and stay-at-home public health orders. In Manitoba, Canada, visit rates were examined at a 24-hour mental health Crisis Response Centre (CRC) that offered in-person and virtual crisis assessments in a stepped care model during the COVID-19 pandemic. Methods All visits from the three years prior to the pandemic until September 28, 2022 were retrieved from the electronic patient record. Mean weekly visits had the pandemic not occurred were predicted with an autoregressive integrated moving average model and compared with observed rates. Results Total pre-pandemic CRC visits (14,280) decreased 22.1%–11,122 total post-pandemic CRC visits. Visit rates remained lower than predicted throughout the observation period, with the total number of visits reduced by an average of 34.1 per week (p < .001) during the first pandemic wave, and that gap narrowing to an average of 18.9 visits per week (p = 0.001) during the fourth wave. Thirteen percent of pandemic visits were virtual; highest during the first wave (average of 34.1% of visits per week) and decreased to an average of 5.6% of visits per week during the last measured period. Discussion Further investigation is necessary to better understand this sustained pattern of reduced service utilization as we move beyond the pandemic.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".