Bibliographic record
Abstract
The COVID-19 pandemic has revealed the interdependence of children's schooling and their mental health, with each child being unique in response to pandemic-related disruptions.There is a will and a financial path forward to work across sectors to promote a mental healthcare model that champions the unique challenges facing each child.This model requires individualized care plans and proactive outreach to children who are reluctant to disclose their suffering. Changing Assumptions about Childhood Mental HealthAn influential report entitled COVID-19: Recommendations for School Reopening was published by The Hospital for Sick Children (SickKids) in Toronto five months after Canada's first reported case of COVID-19 (Science and Bitnun 2020).The report put the spotlight on childhood mental health in a way that eclipsed the media attention paid to later scientific papers on the same topic.Due to the lockdown and associated school closures, the SickKids' report speculated that, because schools were closed, "increased rates of depression, trauma, drug abuse and addiction and even suicide can be anticipated" (Science and Bitnun 2020: 3).The June 2020 report was cited by many observers across North America to argue that all children need in-person schooling and that school closures carry severe mental health consequences for all children.Absent from most media commentary on this report was a reference to its conclusion:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".