The Elephant in the Nursery: Paediatric Exceptionalism?
Bibliographic record
Abstract
Prior to the COVID-19 pandemic (hereafter, ‘the pandemic’), there was already widespread concern about the adequacy of health care resources across Canada. The COVID-19 pandemic exacerbated these concerns exponentially, widening already significant cracks in provincial health care systems. Currently the system is struggling with the exacerbation of wait times for surgeries previously delayed by mandated closures during the pandemic. In Ontario, the backlog of surgeries, and associated backlogs in radiology and other services critical to paediatric care, led to the creation of a consortium of paediatric hospitals committed to advocacy for more funding for paediatrics. Thus far, the provincial and federal governments have agreed to a one-time cash infusion, but the consortium is calling for a permanent increase in funding for paediatrics. A challenge is that the adult sector has also suffered from delays and backlogs. Furthermore, as already noted, older adults have borne the brunt of morbidity and mortality associated with COVID-19. The challenge for the paediatric sector is whether and how to defend the prioritization of children and youth. In this paper, we review four approaches to just allocation – utilitarian ageism, fair innings, the prudential lifespan approach, and prioritarian ageism – and examine their strengths and weaknesses. We conclude by endorsing prioritarian ageism (prioritarianism). Prioritarianism retains the strengths of utilitarian ageism and fair innings while avoiding their weaknesses. Furthermore, because prioritarianism does not treat age as an independent moral criterion, allocation schemes based on this foundation are less susceptible to legal challenge and may be more palatable to the general public.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".