For health or for profit? Understanding how private financing and for-profit delivery operate within Canadian healthcare (4H|4P): protocol for a multimethod knowledge mobilisation research project
Bibliographic record
Abstract
INTRODUCTION: Privatisation through the expansion of private payment and investor-owned corporate healthcare delivery in Canada raises potential conflicts with equity principles on which Medicare (Canadian public health insurance) is founded. Some cases of privatisation are widely recognised, while others are evolving and more hidden, and their extent differs across provinces and territories likely due in part to variability in policies governing private payment (out-of-pocket payments and private insurance) and delivery. METHODS AND ANALYSIS: This pan-Canadian knowledge mobilisation project will collect, classify, analyse and interpret data about investor-owned privatisation of healthcare financing and delivery systems in Canada. Learnings from the project will be used to develop, test and refine a new conceptual framework that will describe public-private interfaces operating within Canada's healthcare system. In Phase I, we will conduct an environmental scan to: (1) document core policies that underpin public-private interfaces; and (2) describe new or emerging forms of investor-owned privatisation ('cases'). We will analyse data from the scan and use inductive content analysis with a pragmatic approach. In Phase II, we will convene a virtual policy workshop with subject matter experts to refine the findings from the environmental scan and, using an adapted James Lind Alliance Delphi process, prioritise health system sectors and/or services in need of in-depth research on the impacts of private financing and investor-owned delivery. ETHICS AND DISSEMINATION: We have obtained approval from the research ethics boards at Simon Fraser University, University of British Columbia and University of Victoria through Research Ethics British Columbia (H23-00612). Participants will provide written informed consent. In addition to traditional academic publications, study results will be summarised in a policy report and a series of targeted policy briefs distributed to workshop participants and decision/policymaking organisations across Canada. The prioritised list of cases will form the basis for future research projects that will investigate the impacts of investor-owned privatisation.
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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 | 0.109 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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".