Applying an ethical lens to the provision of care in for-profit healthcare facilities
Bibliographic record
Abstract
In early 2023, after three years of pandemic and delayed care, Ontario faced an overwhelming backlog of elective surgical procedures and unacceptable wait times. With hospitals experiencing historic health human resources shortages and critical capacity limitations, disruptive change was required. The Ontario government proposed to address these mounting access-to-care issues by paying for-profit healthcare clinics and surgi-centres to provide insured services, resulting in considerable controversy, much opposition, some praise, and many public protests. Previous experiences with for-profit independent health facilities had generated both complaints and documented problems with their operations. This article examines these concerns against the ethical principles of autonomy, beneficence, non-malfeasance, and justice. While much of this unease can be effectively addressed through collaboration and oversight, the complexity and costs involved in ensuring equity and quality may make it difficult for such facilities to maintain profitability.
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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 | 0.044 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.036 | 0.163 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".