Canadian Healthcare System and Individuals with Severe Mental Disorders During Coronavirus Disease 2019: Challenges and Unmet Needs
Bibliographic record
Abstract
The Coronavirus Disease 2019 (COVID-19) pandemic is a syndemic of viral infection and mental health adversity. The pandemic has exacerbated inequalities of access to care in vulnerable populations within the Canadian mental healthcare system. Primary care services are first-line health services in Canada, and are necessary to access specialized services. However, as a result of the limited availability of primary health services, and subsequently, specialized providers (eg, psychiatrists), the demand for these services outweigh the supply. Hitherto, timely access to appropriate services has been cited as a common challenge in Canada as a result of limitations as it relates to resources and in-person activities and support services. While there has been an increase in virtual care opportunities, concerns have been raised with respect to the digital divide. Moreover, while individuals with serious mental illness (SMI) and psychosis are at an increased risk for hospitalization and death from COVID-19, testing and vaccination services have not been prioritized for this population. Taken together, increased funding for mental health service delivery should be emphasized especially for individuals with SMI. There should also be a focus on increased collaboration among individuals with lived experience and health care providers to ensure future policies are developed specifically for this population. Addressing the social determinants of health and prioritizing a continuum of care across various stakeholders may lead to strong integration of care both during and after 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".