Graduates’ Reflections on Professionalism and Identity: Intersections of Race, Gender, and Activism
Bibliographic record
Abstract
Phenomenon: Professionalism as a construct is weaponized to police and punish those who do not fit the norm of what a medical professional should look like or behave, more so when medical professionals in training engage in protests for social justice. In addition, professionalism silences trainees, forcing them not to question anything that looks or feels wrong in their eyes. Socialization in medicine, in both the undergraduate and postgraduate training spaces, poses challenges for contemporary medical professionals who are expected to fit the shape of the ‘right kind of doctor.’ Intersectionality seems to impact how medical trainees experience professionalism, be it intersections of gender, race, how they dress or adorn themselves, how they carry themselves and who they identify as. Although there is literature on the challenges pertaining to professionalism, not much has been written about the weaponization of professionalism in medical training, particularly in the South African context. There is also a paucity of data on experiences of professionalism during or after social upheaval. Approach: This is part of a study that explored the experiences of professionalism of five medical trainees during protests and after protests, extending into their postgraduate training. The main study had 13 participants, eight students and five graduates, who were all interviewed in 2020, five years after the #FeesMustFall protests. For the five postgraduate participants, we looked at how gender, race, hairstyles, adornment, and protests played out in the experiences of professionalism as medical trainees at a South African university. We employed a qualitative phenomenological approach. An intersectional analytical lens was used in analyzing the transcripts of the five graduate participants. Each transcript was translated as the story of that participant. These stories were compared, looking for commonalities and differences in terms of their experiences. Findings: The participants, four males (three Black and one white) and one Black female, were victimized or judged based on their activism for social justice, gender, and race. They were made to feel that having African hairstyles or piercings was not professional. Insights: Society and the medical profession has a narrow view of what a doctor should look like and behave – it should not be someone who wears their hair in locks, has body piercing, or is an activist, least of all if she is a woman, as professionalism is used as a weapon against all these characteristics. Inclusivity should be the norm in medical education.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".