Bibliographic record
Abstract
R ecently, we have all seen myriad articles in the national newspapers announcing that provinces' healthcare systems are imploding, with authors describing systems in states of "crisis" (Laverly 2022), "visibly coming apart" (Tumilty 2022), "broken" (Urback 2022) and "a travesty" (Picard 2022).Without minimizing the hardships or frustrations that some patients and their families have been experiencing at the hands of provincial health systems, are the harsh descriptors apt or fair?Granted that wait times for surgery and emergency department treatment have been problems for a long time, is what we are witnessing in provinces really representative of healthcare systems imploding?Before the COVID-19 pandemic, there were instances of patients' health deteriorating while waiting, and even a small risk of death.Hallway medicine was such a problem in Ontario pre-pandemic that a commission was struck to solve the problems that caused the practice (Premier' s Council on Improving Healthcare and Ending Hallway Medicine 2019).So, it seems fair to ask whether recent events tip the scale from "serious problems" to "implosion?"If the serious nature of the claim is unpacked, what does health system implosion look like in Canada?It would not be financial in nature as governments would continue to pay their bills.This means that hospitals would continue to be open, their staff would continue to be paid, equipment would be purchased and serviced, physicians would be remunerated and provincial drug plans would continue insuring pharmaceuticals.Publicly funded healthcare -hospitals and physician care -would continue as we now know it, for better or worse.Stepping away from the direst descriptions, serious healthcare delivery problems remain but have to come from directions other than financial.The most likely candidates include shortages of specialized staff, a "quality" catastrophe where many perish due to inattentiveness or lack of clinical oversight, a widespread technology failure affecting critical healthcare infrastructure or, possibly, a combination of all these causes.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.024 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".