Canucks versus Yankees: the Case for Universal Health Care over Private Health Care and How Psychiatry Benefits
Bibliographic record
Abstract
As an American –born –raised -trained psychiatrist who has practiced in the United States and now practicing in Canada, it affords me a unique perspective on the health care debates occurring in my home country. The debate regarding health care reform in the States has been heated on both sides. Even doctors are entrenched in one of the opposing positions, with the majority of U.S. physicians supporting reform with a public option [1]. However, it is difficult to discern fact from propaganda on either side, for or against universal health care. One particular problem is the misinformation and fear-mongering that has been covered regarding Canadian universal health care. In this commentary, I will attempt to make the case for universal health care, and focus specifically on the benefits of universal health care for psychiatry. I want to make the case that corporate medicine is detrimental to psychiatry, and how psychiatry practiced within a universal health care system is substantially different from that practiced within a managed care system.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.064 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.025 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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".