Supporting marginalised children with school problems in the COVID-19 pandemic
Bibliographic record
Abstract
In March 2020, the WHO’s declaration of the COVID-19 global pandemic1 resulted in unprecedented public health recommendations to minimise viral spread. This included a major disruption in the cornerstone of children’s lives and well-being—school closures. School boards have since sought to implement a range of novel measures to minimise viral transmission while maintaining access to education. Today, students have the option of learning via virtual learning platforms, in person or through hybridised virtual and in-person models. For the first time in decades, the conventional model of education delivery has undergone rapid change while simultaneously the COVID-19 pandemic has unveiled and exacerbated existing inequities for children with school problems. Consequently, healthcare providers must adapt their response to school-based problems during the pandemic. They must also use lessons learnt to re-invent an approach to address inequities in caring for the 10%–15% of children who will present with these issues at some point in their school years.2 Children with learning, behavioural and social–emotional problems require careful assessment of their educational environment and socioeconomic circumstances. The learning ecosystem is informed by teachers and school paraprofessionals, while social risks are determined by careful history taking and screening. Distance learning however presents challenges for educators to characterise educational, behavioural and developmental needs. Additionally, school support staff such as educational assistants, speech and language pathologists, occupational therapists and psychologists may not be able to provide a comprehensive assessment using virtual platforms. Moreover, nearly 15% of children in the USA lack reliable access to broadband internet and many do not have …
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".