Commentary: One Strategy for Building Public Trust in Changing Times
Bibliographic record
Abstract
Major health care reforms are being debated in the United States. While these debates address issues of access and cost, the systems-based problems of patient safety, continuous quality improvement, and an integrated approach to continuing professional development (CPD) remain traditional opportunities for the profession to directly improve health care and maintain professional accountability. Such challenges can be addressed independently of proposed reforms and offer an opportunity for the profession to build greater public trust. Given recent evidence questioning many assumptions behind individually focused CPD, and as physicians' work shifts into group and team contexts, it is an opportune time to address better CPD strategies within emerging group and team settings.The first strategic change requires a focus on managing the development of the individual physician's educational growth into a systems-oriented approach based on better information and feedback within groups of practitioners and health care teams. Second, the renewal of the linked visions of professional collegiality and accountability with professional regulation needs to be seen as a collective responsibility across key organizations within the profession's normal accountability framework. Thus, the professional colleges, certifying boards, and regulatory authorities need to collaborate with the CDP community in refocusing their collective activities to promote the profession's traditional role of improving the quality of care and maintaining the public's trust in these times of intense policy debate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.089 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.079 | 0.077 |
| Insufficient payload (model declined to judge) | 0.007 | 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".