Attitudes and knowledge regarding health care policy and systems: a survey of medical students in Ontario and California
Bibliographic record
Abstract
BACKGROUND: Canada and the United States have similar medical education systems, but different health care systems. We surveyed medical students in Ontario and California to assess their knowledge and views about health care policy and systems, with an emphasis on attitudes toward universal care. METHODS: A web-based survey was administered during the 2010-2011 academic year to students in 5 medical schools in Ontario and 4 in California. The survey collected demographic data and evaluated attitudes and knowledge regarding broad health care policy issues and health care systems. An index of support for universal health care was created, and logistic regression models were used to examine potential determinants of such support. RESULTS: Responses were received from 2241 students: 1354 from Ontario and 887 from California, representing 42.9% of eligible respondents. Support for universal health care coverage was higher in Ontario (86.8%) than in California (51.1%), p < 0.001. In California, females, self-described nonconservatives, students with the intent to be involved in health care policy as physicians and students with a primary care orientation were associated with support for universal coverage. In Ontario, self-described liberals and accurate knowledge of the Canadian system were associated with support. A single-payer system for practice was preferred by 35.6% and 67.4% of students in California and Ontario, respectively. The quantity of instruction on health care policy in the curriculum was judged too little by 73.1% and 57.5% of students in California and Ontario, respectively. INTERPRETATION: Medical students in Ontario are substantially more supportive of universal access to health care than their California counterparts. A majority of students in both regions identified substantial curricular deficiencies in health care policy instruction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".