Bibliographic record
Abstract
T he spring has ushered in an unexpected number of major health policy announcements compared with the last 10 years.They are led by the federal government' s outlines of a national pharmacare program, an unexpected dental care program (Prime Minister of Canada Justin Trudeau 2022), plus "top-up" funding for clearing provincial surgical and imaging backlogs.These announcements are on top of the voices expressing concerns about COVID-19-related healthcare expenditure trends (Bailey 2022).Without a doubt, taxpayer money is flowing freely into healthcare (Labby 2020).The major policy initiatives occur in the backdrop of a receding pandemic whose social and economic policies to combat the spread of COVID-19 wrought a severe toll on many Canadians.The impact has not been equally distributed -infirm seniors, the economically or socially marginalized, healthcare workers and others who disproportionately experienced the pandemic' s burden.From my vantage point, I am surprised by the lack of anger and frustration with the provincial and territorial healthcare delivery systems' return to the same modus operandi.I am surprised that there are no loud voices demanding meaningful reform among the families of long-term care residents isolated during the pandemic, among dependents of overworked healthcare professionals shuffled into gut-wrenching situations and among surgical patients enduring prolonged waits for surgery, who are now also demonstrating symptoms of depression and anxiety (HQCA 2019; Rubinoff 2022; Silas and McKenna 2021).As public spending on health services and products accelerates with these policy announcements, I expected a commensurate quid pro quo, with taxpayers demanding that the foundation of provincial healthcare delivery be strengthened.New government spending should purchase not only more of the same but some of the new funding should fix, or meaningfully reduce, the silo-based delivery system' s faults.Many are clear about the most important problems facing healthcare delivery in Canada.Though researchers may argue over the relative ranking of problems, the list of suspects usually New Spending Programs and Old Frustrations:Where Is the Vision?
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".