Calling for a Broader Conceptualization of Diversity
Bibliographic record
Abstract
PURPOSE: Policy groups recommend monitoring and supporting more diversity among medical students and the medical workforce. In Canada, few data are available regarding the diversity of medical students, which poses challenges for policy development and evaluation. The authors examine diversity through a framework of surface (visible) and deep (less visible) dimensions and present data regarding a sample of Canadian medical students. METHOD: Between 2009 and 2011, nine cohorts from four Canadian medical schools completed the Health Professions Student Diversity Survey (HPSDS) either on paper or online. Items asked each participant's age, gender, gender identity, sexual identity, marital status, ethnicity, rural status, parental income, and disability. Data were analyzed descriptively and compared, when available, with national data. RESULTS: Of 1,892 students invited, 1,552 (82.0%) completed the HPSDS. Students tended to be 21 to 25 years old (68.3%; 1,048/1,534), female (59.0%; 902/1,529), heterosexual (94.6%; 1,422/1,503), single (90.1%; 1,369/1,520), and unlikely to report any disability (96.5%; 1,463/1,516). The majority of students identified with the gender on their birth certificate (99.8%; 1,512/1,515). About half had spent the majority of their lives in urban environments (46.7%; 711/1,521), and most reported parental household incomes of over $100,000/year (57.6%; 791/1,373). Overall, they were overrepresentative of higher-income groups and underrepresentative of populations of Aboriginal, black, or Filipino ethnicities in Canada. CONCLUSIONS: The authors propose the development of a National Student Diversity Database to support both locally relevant policies regarding pipeline programs and an examination of current application and selection procedures to identify potential barriers for underrepresented students.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".