Exploring medical students’ experiences with indigenous patient care: a phenomenological study
Bibliographic record
Abstract
BACKGROUND: Indigenous Peoples in Canada bear a disproportionate burden of disease and experience poorer health outcomes as compared to non-Indigenous populations within Canada; these conditions are said to be mediated and exacerbated by pervasive and uninterrupted anti-Indigenous racism. Third and fourth-year medical students at a Canadian medical school were asked to reflect on their experiences working with Indigenous patients in clinical settings, and how their preclinical Indigenous health curriculum impacted these experiences. METHOD: Phenomenology was used, guided by Goffman's theory of social stigma. Eleven undergraduate medical students were recruited using purposeful sampling. Semi-structured phone interviews were conducted to gain an in-depth understanding of the participants' experiences. Interviews were recorded and transcribed verbatim. Data were analyzed using the four main processes for phenomenological analysis. RESULTS: Four main themes emerged from students' descriptions of clinical experiences involving Indigenous patients: 1) students describe how their Indigenous patients encounter the health care system and their own lack of cultural sensitivity in this context; 2) racism was evident in students' clinical interactions with Indigenous patients, but students do not always perceive these biases nor the impact of this on patient care; 3) identifying the impact of racism on care is complicated by situational clinical encounters; and 4) practicality of preclinical Indigenous health education is desired by students to prepare them for working with Indigenous patients in the clinical setting. CONCLUSIONS: In their clinical experiences, students witness racism against Indigenous peoples yet are unprepared to stand up against it. Findings highlight the importance of enhancing undergraduate medical training to allow students to better understand the unique experiences and perspectives of Indigenous patients. The results support the need for ongoing Indigenous health education, to foster culturally sensitive experiences while learning about Indigenous 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".