Impact of COVID-19 School-Related Policies in Ontario on Parents’ School Lunch Packing Habits
Bibliographic record
Abstract
Purpose: COVID-19 has disrupted the daily routines of many Canadian families. In Ontario, provincially mandated public health measures have resulted in significant changes to school policies, including those related to food. The impact of COVID-19 related school food policies on parental lunch packing habits is unknown; therefore, this study investigated how school-related COVID-19 policies have impacted parental school lunch packing habits. Methods: Parents (N = 287) of school-aged children were recruited from parent-specific Facebook groups across Ontario, Canada, to complete an online survey regarding lunch packing habits. This survey was developed based on findings from a previously conducted scoping review. Open-ended survey responses were inductively analyzed. Results: Three over-arching themes were constructed: (1) Food Programs and COVID-19; (2) Schedule Changes; and (3) School Policy Changes. Parents explained that the cancellation or modification of food programs at schools, changes to the length of time children are given to eat at school, and removal of access to microwaves, garbage cans, and teacher assistance during lunch have forced parents to change their lunch packing habits. Conclusion: Findings from this study demonstrate a need for better support to help ease the burden parents experience when packing their child’s school lunch, during an already extremely stressful time.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".