School Attendance Among Pediatric Oncology Patients During the COVID-19 Pandemic in Ontario, Canada
Bibliographic record
Abstract
Supporting schooling for current and past pediatric oncology patients is vital to their quality of life and psychosocial recovery. However, no study has examined the perspectives toward in-person schooling among pediatric oncology families during the COVID-19 pandemic. In this online survey study, we determined the rate of and attitudes toward in-person school attendance among current and past pediatric oncology patients living in Ontario, Canada during the 2020-2021 school year. Of our 31-family cohort, 23 children (74%) did attend and 8 (26%) did not attend any in-person school during this time. Fewer children within 2 years of treatment completion attended in-person school (5/8; 62%) than those more than 2 years from treatment completion (13/15; 87%). Notably, 22 of 29 parents (76%) felt that speaking to their care team had the greatest impact compared to other potential information sources when deciding about school participation, yet 13 (45%) were unaware of their physician's specific recommendation regarding whether their child should attend. This study highlights the range in parental comfort regarding permitting in-person schooling during the COVID-19 pandemic. Pediatric oncologists should continue to address parental concerns around in-person school during times of high transmission of COVID-19 and potentially other communicable diseases in the future.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".