A policy analysis of Bill-124 and the nursing shortage
Bibliographic record
Abstract
Nurses comprise the largest portion of the healthcare workforce across Canada, yet there have been ongoing shortages for the last decade. This shortage has been pronounced with the recent COVID-19 pandemic. Healthcare spending has also been increasing steadily in Canada. The Canadian provincial governments, such as Ontario, see this as an opportunity to stabilize its fiscal healthcare spending by implementing a policy to freeze nurses' wages. The focus of this policy analysis is to address the question: how did Bill-124 reach the Ontario government's agenda in the midst of a nursing shortage? Why was this specific policy action successful in being implemented as a possible solution to remediate provincial debt burden? The authors will be using the Kingdon's framework to help analyze this policy. They will also articulate the impacts of such government decisions; and provide recommendations with strategies on how to tackle the challenge.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".