Operational status of mental health, substance use, and problem gambling services: A system‐level snapshot two years into the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: The aim of this paper is to provide a system-level snapshot of the operational status of mental health, substance use, and problem gambling services 2 years into the pandemic in Ontario, Canada, with a specific focus on services that target individuals experiencing vulnerable circumstances (e.g., homelessness and legal issues). METHODS: We examined data from 6038 publicly funded community services that provide mental health, substance use, and problem gambling services in Ontario. We used descriptive statistics to describe counts and percentages by service type and specialisation of service delivery. We generated cross-tabulations to analyse the relationship between the service status and service type for each target population group. RESULTS: As of March 2022, 38.4% (n = 2321) of services were fully operational, including 36.0% (n = 1492) of mental health, 44.1% (n = 1037) of substance use, and 23.4% (n = 78) of problem gambling services. These service disruptions were also apparent among services tailored to sexual/gender identity (women/girls, men/boys, 2SLGBTQQIA + individuals), individuals with legal issues, with acquired brain injury, and those experiencing homelessness. CONCLUSION: Accessible community-based mental health, substance use and problem gambling services are critical supports, particularly for communities that have historically contended with higher needs and greater barriers to care relative to the general population. We discuss the public health implications of the findings for the ongoing pandemic response and future emergency preparedness planning for community-based mental health, substance use and problem gambling services.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".