Lessons from the "non-critical" patient during a pandemic: developmental-behavioural pediatric populations & COVID-19
Bibliographic record
Abstract
Seemingly overnight, in March 2020, the world was turned upside down by the global SARS-CoV 2 (novel coronavirus) pandemic. As COVID-19 affected all aspects of clinical care, Canadian ambulatory clinics for any service deemed "non-urgent" or "non-critical," were suspended for several months. When outpatient care slowly resumed during the summer and fall of 2020, the backlog of patients in these areas and subsequent requests for follow-up significantly outpaced the number of available appointments. In fact, it became apparent that certain patients' needs had grown in unprecedented ways during the pandemic, even though their issues had previously been given low priority during the acute crisis period. Among these groups were youth with underlying mental health conditions, those with chronic but non-life-threatening illnesses, and the subgroups seen in Developmental-Behavioural Pediatrics. In Montréal, they were among the least likely to have their needs met as the waves of COVID-19 moved through the community, and many still struggle to find relevancy in the discussions about the hidden impacts of the coronavirus pandemic, even one year later. What can the experiences of these marginalised youth teach us about what our system labels less relevant care in the context of an acute health care crisis? A short narrative presentation will demonstrate insights gleaned from 2020 & early 2021 to underscore the often unrecognised challenges faced by these populations and their families.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".