Associations Between Receipt of the Canada Recovery Benefit and Mental Health in Canadian Parents and Their Children
Bibliographic record
Abstract
Background: The COVID-19 pandemic was associated with a large economic downturn in Canada. Past economic recessions and unemployment have been associated with poorer mental health. Therefore, the Canada Recovery Benefit (CRB), a benefit meant to support Canadians who lost employment or income due to COVID-19, may attenuate declines in mental health. Objectives: 1) Examine if the CRB is associated with an attenuation in declines of mental health and functioning in Canadian parents and their children. 2) Determine whether this association differs for those in low-income versus non-low-income households. Methods: This was a secondary data analysis using the longitudinal Canadian Health Survey on Children and Youth which collected data in 2019 and 2023. Multi-level logistic regression was conducted to examine whether receipt of the CRB was associated with attenuation of within-person declines in parent self-reported mental health, and parent-reported child cognitive-behavioural, and emotional functioning. Results: There was a significant increase in the odds of reporting poor parental mental health from 2019 to 2023. Findings for main objectives are generally non-significant and inconclusive, although there is some small evidence showing that the CRB may be associated with an attenuation of mental health decline. A sensitivity analysis of households with single parents resulted in stronger trends suggesting an attenuation of mental health decline, though still non-significant. No significant differences in the association of the CRB with changes in mental health or functioning were observed between low-income and non-low-income households. Conclusion: Associations between the CRB and attenuation of mental health and functioning declines are unclear, though a single-parent sensitivity analysis gave a limited indication that the CRB could be associated with a non-significant attenuation of mental health decline. Future research should address methodological limitations and consider broadening its scope to other pandemic-era economic supports.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".