Personal autonomy and self‐determination are crucial for professionalism in healthcare
Bibliographic record
Abstract
In our role as medical educators and researchers, we support in the strongest manner possible the personal autonomy and self-determination of our students, patients, and colleagues. A fundamental value of the medical profession is for the patient to have personal autonomy in their healthcare decisions, including how they would like to be identified. It is, and will continue to be, an important priority to be taught and encouraged throughout healthcare education,1 and it begins in the anatomy laboratory with the proper and respectful treatment of body donors.2 Learning this value continues with respecting and accepting this same autonomy in fellow students, staff, faculty, and patients. There are some who question whether foregrounding the value of personal autonomy and the right to self-identification in health professions education is an appropriate use of resources and teaching time in an already crowded curriculum. Let us be clear: understanding this value and addressing it in the correct language3 are of paramount importance to the well-being of all involved in healthcare and should be a recurring theme throughout their education and career.4 Personal autonomy is one of the four pillars of bioethics and is a strong antidote to the paternalism and mistakes of recent eras of healthcare delivery. Any individual should be able to make their personal identity known without fear or ridicule, and this is especially important in healthcare fields. Time and resources should be devoted to educating our future healthcare professionals in the proper way to address patients, students, and colleagues. This should occur during all phases of healthcare education and should be practiced and exemplified by those who teach in healthcare. Appropriate identification of individuals, and their right to choose how they wish to be identified, is a fundamental tenet of healthcare professionalism and demands recognition as an integral principle of human rights. Complete healthcare starts with a thorough understanding of the patient, and that includes respect of personal autonomy. Acquiring skills and knowledge around the appropriate application of such principles is necessary as one of many essential components of medical education and training.
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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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.069 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".