Association between neighbourhood composition, kindergarten educator-reported distance learning barriers, and return to school concerns during the first wave of the COVID-19 pandemic in Ontario, Canada
Bibliographic record
Abstract
Introduction: Research to date has established that the COVID-19 pandemic has not impacted everyone equitably. Whether this unequitable impact was seen educationally with regards to educator reported barriers to distance learning, concerns and mental health is less clear. Objective: The objective of this study was to explore the association between the neighbourhood composition of the school and kindergarten educator-reported barriers and concerns regarding children's learning during the first wave of COVID-19 related school closures in Ontario, Canada. Methods: = 2569; 74.2% kindergarten teachers, 25.8% early childhood educators; 97.6% female) using an online survey asking them about their experiences and challenges with online learning during the first round of school closures. We linked the educator responses to 2016 Canadian Census variables based on schools' postal codes. Bivariate correlations and Poisson regression analyses were used to determine if there was an association between neighbourhood composition and educator mental health, and the number of barriers and concerns reported by kindergarten educators. Results: There were no significant findings with educator mental health and school neighbourhood characteristics. Educators who taught at schools in neighbourhoods with lower median income reported a greater number of barriers to online learning (e.g., parents/guardians not submitting assignments/providing updates on their child's learning) and concerns regarding the return to school in the fall of 2020 (e.g., students' readjustment to routines). There were no significant associations with educator reported barriers or concerns and any of the other Census neighbourhood variables (proportion of lone parent families, average household size, proportion of population that do no speak official language, proportion of population that are recent immigrants, or proportion of population ages 0-4). Conclusions: Overall, our study suggests that the neighbourhood composition of the children's school location did not exacerbate the potential negative learning experiences of kindergarten students and educators during the COVID-19 pandemic, although we did find that educators teaching in schools in lower-SES neighbourhoods reported more barriers to online learning during this time. Taken together, our study suggests that remediation efforts should be focused on individual kindergarten children and their families as opposed to school location.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".