Early pandemic impacts on family environments that shape childhood development and health: A Canadian study
Bibliographic record
Abstract
OBJECTIVES: Changes to income and employment are key social determinants of health that have impacted many families during the COVID-19 pandemic. This research aimed to understand how changes to employment and income influenced family environments that contribute to early childhood development and health. METHODS: A concurrent triangulation mixed method design was used through a cross-sectional survey on early impacts of the COVID-19 pandemic involving families with young children in the Canadian Maritime provinces (n = 2158). Analyses included multivariate regression models to examine whether changes to employment and income predicted changes to Family access to resources and social support, parenting Abilities and self-care at home, and home Routines and Environments (FARE Change Scale). Content analysis was used to identify themes from the open-ended questions. RESULTS: Changes to employment and income early in the pandemic like no longer working but continued to receive salary, working fewer hours for the same salary earned before the pandemic, no longer working nor receiving salary, working fewer hours resulting in salary reduction, essential worker status and household income were significant predictors of FARE Change Scale when ethnicity/cultural background and province of residence are controlled (P < .05). Themes provided a description of family impacts, including shifting employment and income, finding time and capacity, feelings of guilt and the creation of new routines. CONCLUSION: Our study provides insight on the implications of public health restrictions, such as the importance of increased time for parents (through reduced work hours) and access to resources and social support to support child development and health.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.017 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".