Parenting during the COVID-19 pandemic: The sociodemographic and mental health factors associated with maternal caregiver strain.
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has introduced new stressors for parents ("caregivers") that may affect their own and their child's mental health (MH). We explored self-reported levels of caregiver strain (parents' perceived ability to meet parenting demands), and the MH and sociodemographic factors of caregivers to identify predictors of strain that can be used to guide MH service delivery for families. METHODS: We administered a web-based survey to Ontario caregivers with a child between 4 and 25 years old, between April and June 2020. We analyzed information from 570 maternal caregivers on their sociodemographics, youngest (or only) child's MH, their own MH, and the degree of caregiver strain experienced since the pandemic. We used linear regressions (unadjusted and adjusted models) to explore the relationship between caregiver strain and sociodemographics, child MH and caregiver MH. RESULTS: Over 75% of participants reported "moderate-to-high" caregiver strain. More than 25% of caregivers rated their MH as "poor" and 20% reported moderate-to-severe anxiety. Forty-five percent of the variance in caregiver strain was accounted for by child age, caregiver anxiety, and multiple child and caregiver MH variables. Younger child age and higher caregiver anxiety were the greatest predictors of caregiver strain. CONCLUSION: We found a relationship between child age, child and caregiver MH variables, and caregiver strain. Given the interrelatedness of these factors, supporting caregivers' MH and lessening their role strain becomes critical for family well-being. Evidence-based individual, family, and public health strategies are needed to alleviate pandemic-related strain. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".