Evaluating the effect of the Progressive Conservative Party of Manitoba's austerity-centred approach on Manitoba's healthcare system from 2016 to 2022
Bibliographic record
Abstract
Austerity – a set of economic policies intended to reduce or eliminate yearly budget deficits and diminish the overall size of the state – is often enacted by governments when they believe saving, rather than spending, is the most effective way to stimulate the economy. The effects of austere policies, such as budget cuts, privatizations, and contracting out, have been and continue to be the subject of debate and exploration amongst policymakers, economists, and scholars alike. This paper examines the effect of austerity on the healthcare system – specifically on healthcare workers and their ability to deliver care – in Manitoba in the years since 2016, when the Progressive Conservative Party of Manitoba (PCP) reclaimed governing power from the New Democratic Party of Manitoba (NDP). It analyzes a series of quantitative measures of Manitoba’s healthcare system funding and performance, including expenditures, wait times, and job vacancy rates within the sector, supplemented by a qualitative analysis of survey responses from more than 450 of the province’s healthcare workers. It shows that 1) the PCP has included healthcare as part of its broad austerity agenda and 2) the effects on healthcare workers and their ability to provide care have been and continue to be overwhelmingly negative, which accords with the existing literature on austerity and healthcare. Although most provinces have found their healthcare systems in crisis in recent years, the findings presented in this paper suggest that Manitoba under an austerity-focused-PCP fares worse than the national average by almost every measure.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".