Public Interest, Patient Engagement and the Transparency Initiative of the College of Physicians and Surgeons of Ontario
Bibliographic record
Abstract
In recent years, patient-centredness has become a central focus in improving health care quality. In 2010, the Canadian Medical Association (CMA) launched a four-year action plan aimed at transforming Canada’s health care through a framework aimed at creating a culture of patient-centred care, accountability and responsibility. Several of Canada’s provincial governments proceeded to launch patient-centred action plans, including the Government of Ontario’s “Patients First” framework, which prioritizes patient engagement and increased transparency. As an example of how organizations are putting these values into practice, the College of Physicians and Surgeons of Ontario (CPSO)’s transparency initiative, which aims to make more physician-specific information available to the public in order to help patients make informed decisions about their health care, was examined. This thesis asks how physicians and members of the public feel that the transparency initiative aligns with the CPSO’s public interest mandate. Using discourse analysis, 226 responses by physicians, members of the public and organizations on a discussion forum in the Policy Consultations section of the CPSO’s website were analyzed in order to identify the main themes in arguments for or against increased transparency. The results show that physicians and members of the public tended to differ in their views on the purposes and probable outcomes of the CPSO’s transparency initiative. The majority of physicians worried about patients’ ability to accurately understand and utilize the information being provided to them, and the negative impact that this might have on individual physicians and on the physician-patient relationship more broadly. Most members of the public had a more positive outlook on the potential for transparency to build public trust, help patients become informed and engaged decision-makers and improve patient safety.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.029 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".