Knowledge, psychological impacts, and protective behaviours during the first wave of the COVID-19 pandemic among Chinese residents in Canada with dependent school-age children: a cross-sectional online study
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to describe the knowledge, protective behaviours, and psychological impact of COVID-19 on Chinese residents in Canada, as the emotional and behavioural impacts of the pandemic have not been intensively studied amongst these populations. It was important to determine whether having dependent school-age children (DSAC) aged 16 or under was associated with adverse psychological impacts amongst the Chinese residents living in the country. METHODS: In April 2020, 757 eligible participants were recruited through a snowball sampling to complete an online survey related to the COVID-19 pandemic. Psychological, behavioural, and sociodemographic variables were collected and first analyzed using descriptive and univariate statistics. Multiple logistic regression analyses were performed to further confirm the observed significant associations in bivariate analyses for selected psychological outcome variables. RESULTS: Seven hundred forty-two participants who responded to the "dependent school-age children" question were included in the analysis. Most of them identified as females (65.8%) and 77.2% included receiving a university degree or higher. There were no significant differences in COVID-19 knowledge between those living with or without DSAC. However, participants with DSAC were more likely to perceive themselves as being at greater risk of contracting COVID-19 (p = .023); therefore, having a higher chance of adopting protective behaviours (e.g., hand washing, sanitizing frequently or disinfecting work and living spaces (p < .05), elevated risks of depression (p = .007), and stress (p = .010), compared to those without DSAC. CONCLUSIONS: Predominantly, the Chinese residents in Canada with dependent school-age children were more likely to report the negative psychological impacts of the pandemic. These findings warrant further investigations that may contribute to informing key stakeholders about the identification and implementation of policies and interventions to support the needs of parents with young children, during and after the pandemic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".