Who Protects Clinical Learners in Canada? Ethical Considerations for Institutional Policy on Patient Bias
Bibliographic record
Abstract
Navigating the social dynamics of clinical spaces can be an added challenge to the complexities of clinical work. Acts of bias and discrimination from patients have been found to affect healthcare workers both physically and psychologically. As more attention is paid to addressing discrimination by patients, we raise attention to the experiences and unique needs of clinical learners. Given that learners play a vital role in the functioning of hospital ecosystems, we advocate for the inclusion of their voices in any revision to policy and practice. In this paper, we critically examine the academic literature on learner’s experiences with mistreatment from patients, and their families. We outline the major gaps in policy, process, training, and institutional culture, noting the urgent need for institutions to address these gaps in ways that are meaningful to learners. Our goal is to highlight the lack of bioethics attention to this matter and propose areas where we can add value and support. With this goal in mind, we present a series of tables with guiding values, ethical considerations and questions for institutions.
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 | 0.077 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.045 | 0.035 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 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".