Ontario adults’ health behaviors, mental health, and overall well-being during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Public health measures such as physical distancing and work-from-home initiatives have been implemented to slow the spread of COVID-19. These measures may also be associated with unhealthy lifestyle behaviors, which could be particularly problematic for those already at highest risk for losing years of healthy life due to chronic disease (i.e., 30-59-year-olds). The purpose of this paper is two-fold: (1) to provide an overview of Ontario adults' health behaviors (i.e., physical activity, sedentary behaviors, and dietary intake), mental health, and well-being during the first few months of the COVID-19 pandemic (April-July 2020); and (2) to explore the difference between physical activity and various health behaviors (i.e., well-being, mental health, and dietary intake). METHODS: As a part of a larger, longitudinal study, participants completed an online survey that included demographic information, the Global Physical Activity Questionnaire, Starting the Conversation, the Mental Health Inventory, and the Personal Wellbeing Index-Adult. Data analyses involved computing measures of central tendency and dispersion for demographic characteristics and tools followed by descriptive statistics. Separate independent t-tests were conducted to investigate the difference between physical activity status and well-being, mental health, and dietary intake. RESULTS: A total of 2157 Ontarians completed an online survey. Descriptive statistics indicated that respondents met physical activity and sedentary behavior guidelines, reported double the amount of recommended recreational screen time, practiced moderately healthy dietary behaviors, experienced mental health problems, and scored below "normal" in some well-being domains. CONCLUSION: As the end of the COVID-19 pandemic is currently unknown, its associated restrictions and society changes may influence adults' behaviors in both the short- and longer-term. As such, our findings might provide immediate insight into the development of timely and evidence-informed health promotion and disease prevention strategies for Canadians, which could support adults' health behaviors, mental health, and well-being during the COVID-19 pandemic and other, future pandemics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".