The Hidden Realities of Discrimination from Patients: A Scoping Review of Healthcare Workers’ Experiences
Bibliographic record
Abstract
Discrimination in healthcare settings is a burgeoning area of applied inquiry and intervention. Existing research has focused on the experiences of patients as the targets of discrimination with less attention paid to patients as the source of discrimination. The main objective of this scoping review is to identify, explore and map the literature on the experiences of healthcare workers (HCWs) as targets of discrimination from patients and/or their family members. A scoping review of articles indexed in Ovid Medline, Ovid Embase, Ovid Emcare, and Web of Science Core Collection was conducted between March 2022 and June 2023. The results were summarized, coded and thematically categorized according to the aim. The review identified 173 articles that highlighted various forms of discrimination manifesting in a multitude of ways, including requests for, and refusals of specific HCWs based on social identity markers. The results suggest that there are significant barriers that prevent HCWs from reporting and responding to these incidents in efficient ways, resulting in an array of negative psychological ramifications. This review highlights core areas in need of greater attention in order to better support HCWs during challenging interactions with discriminatory patients. Institutional recommendations aimed at research and education efforts, learner experiences, policy writing, documenting and reporting, institutional culture, resources and support as well as the role of professional bodies, were identified. Evidence-informed work is needed in this area to ensure that policy-level changes are informed by the lived experiences of those enduring these incidents.
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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 | 0.029 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".