Cross-sectional survey of the Mental health and Addictions effects, Service impacts and Care needs of children, youth and families during the COVID-19 pandemic: the COVID-19 MASC study protocol
Bibliographic record
Abstract
Introduction The COVID-19 pandemic has had a tremendous negative effect on the mental health and well-being of Canadians. These mental health challenges are especially acute among vulnerable Canadian populations. People living in Canada’s most populous province, Ontario, have spent prolonged time in lockdown and under public health measures and there is a gap in our understanding of how this has impacted the mental health system. This protocol describes the Mental health and Addictions Service and Care Study that will use a repeated cross-sectional design to examine the effects, impacts, and needs of Ontario adults during the COVID-19 pandemic. Methods and analysis A cross-sectional survey of Ontario adults 18 years or older, representative of the provincial population based on age, gender and location was conducted using Delvinia’s AskingCanadians panel from January to March 2022. Study sample was 2500 in phases 1 and 2, and 5000 in phase 3. The Alcohol, Smoking and Substance Involvement Screening Test and Diagnostic Statistical Manual-5 Self-Rated Level 1 Cross-Cutting Symptom Measure-Adult were used to assess for substance and mental health concerns. Participants were asked about mental health and addiction service-seeking and/or accessing prior to and during the pandemic. Analyses to be conducted include: predictors of service access (ie, sociodemographics, mental illness and/or addiction, and social supports) before and during the pandemic, and χ2tests and logistic regressions to analyse for significant associations between variables and within subgroups. Ethics and dissemination Ethics approval was obtained from the Sunnybrook Research Ethics Board. Dissemination plans include scientific publications and conferences, and online products for stakeholders and the general public.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".