The good student or the good patient? The barriers encountered by undergraduate medical students with disabilities at the Northern Ontario School of Medicine
Bibliographic record
Abstract
The American Association of Medical College’s (AAMC) Lived Experience report was released \nin March 2018 with hopes of broadening the diversity of medical students to include more of \nthose with disabilities (Meeks & Jain, 2018). The authors hoped to generate discussion and study \nthe lived experiences of current medical students, residents and practicing physicians with \ndisabilities to learn about the barriers and supports that they have and continue to encounter \nalong their journeys in medicine. In response to the Meeks & Jain (2018) publication, the \npurpose of this study was to replicate their study with the research question “What are the \nbarriers encountered by undergraduate medical students with self-identified disabilities at one \nNorthern Ontario medical school?”. The Lived Experience Project provides a unique opportunity \nto learn about, and compare the experiences of, participants in this study to medical students at \none medical school in Northern Ontario (Meeks & Jain, 2018). In doing so, the climate and \nculture of this school and how this affects the treatment and education of students with \ndisabilities, including the barriers they face in the academic accommodation process, in medical \nenvironments and throughout medical school as a whole were explored. \nA qualitative descriptive study design was used. Data was collected using an initial \ndemographics-based survey followed by a semi-structured interview. Interviews were conducted \nin person or by telephone. Data was transcribed and analysed using Braun & Clarke Thematic \nAnalysis (2013). It was found that the participants of this study found barriers directly associated \nwith their medical education in addition to barriers indirectly associated with their medical \neducation and finally, barriers outside of medical school. Supports in the lives of participants \nwere also identified as a theme in the current research, suggesting a positive impact in the lives \nof medical students with disabilities. No barriers specific to being a student in Northern Ontario \narose, which may be in part to the nature of the sample and small sample size. Implications for \nthis research include reviews of accommodation policies, revision of technical standards at a \nnational and institutional level as well as strengthened communication between the student, the \nmedical school, faculty, and administration.
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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 | 0.000 | 0.000 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".