Navigating discriminatory requests and refusals of healthcare workers: A Canadian-based inpatient hospital algorithm
Bibliographic record
Abstract
BackgroundHealthcare workers are increasingly subject to violence, aggression, and discriminatory requests from patients and families, reflecting broader societal biases within healthcare settings. In response, some institutions have developed policies and decision-making tools to guide leaders in addressing these situations ethically, consistently, and in accordance with human rights obligations.AimThis paper describes the revision of a previously published Caregiver Preference Algorithm to guide healthcare leaders in managing discriminatory patient requests. The goal was to create a more robust, accessible, and contextually sensitive tool to support decision-making.Research designThe algorithm was revised through a multi-phase quality improvement project aimed at enhancing support for both frontline clinicians and leadership.Participants and research contextThe project was conducted at a large, multisite tertiary care hospital in Ontario, Canada. Interviews were completed with 27 healthcare workers from various clinical areas. Stakeholder consultations included clinical and operational leadership, legal counsel, patient relations, equity offices, patient partners, and frontline staff.Ethical considerationsThis project was approved by the University Health Network's Quality Improvement Review Committee [ID: QIRC 22-0378].FindingsThe updated algorithm is structured around six key decision points: (1) patient acuity and capacity; (2) consideration of religious, cultural, or trauma-informed needs; (3) relevance of trainee or learner status; (4) whether the request violates the Human Rights Code; (5) the identity of the requester; and (6) the clinician's willingness to continue care.DiscussionThe revised algorithm integrates legal and ethical principles to help healthcare leaders navigate complex situations. It offers structured guidance while allowing flexibility to respond sensitively to diverse clinical contexts.ConclusionThis work contributes a practical, rights-based framework that can support healthcare institutions in ethically and consistently responding to discriminatory patient requests while protecting healthcare workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".