Survey Shows That At Least Some Physicians Are Not Always Open Or Honest With Patients
Bibliographic record
Abstract
The Charter on Medical Professionalism, endorsed by more than 100 professional groups worldwide and the US Accreditation Council for Graduate Medical Education, requires openness and honesty in physicians' communication with patients. We present data from a 2009 survey of 1,891 practicing physicians nationwide assessing how widely physicians endorse and follow these principles in communicating with patients. The vast majority of physicians completely agreed that physicians should fully inform patients about the risks and benefits of interventions and should never disclose confidential information to unauthorized persons. Overall, approximately one-third of physicians did not completely agree with disclosing serious medical errors to patients, almost one-fifth did not completely agree that physicians should never tell a patient something untrue, and nearly two-fifths did not completely agree that they should disclose their financial relationships with drug and device companies to patients. Just over one-tenth said they had told patients something untrue in the previous year. Our findings raise concerns that some patients might not receive complete and accurate information from their physicians, and doubts about whether patient-centered care is broadly possible without more widespread physician endorsement of the core communication principles of openness and honesty with patients.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".