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Record W4409087251 · doi:10.1080/10401334.2025.2486383

“I knew I was not going to get fired … I know what the line is”: How HPE Faculty Support Trainees’ War Against Social Harm and Injustice

2025· article· en· W4409087251 on OpenAlexaboutno aff
Tasha R. Wyatt, Candace J. Chow, Quang‐Tuyen Nguyen, Emily Scarlett, Ting‐Lan Ma

Bibliographic record

VenueTeaching and Learning in Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
FundersAmerican Educational Research Association
KeywordsHarmInjusticeResistance (ecology)Medical educationPsychologyPublic relationsSociologyMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Introduction: Trainees are adept at locating social harm and injustice in medical education. One of the ways in which they work for change is through ongoing acts of professional resistance. However, knowing how, when, and where to resist requires some institutional knowledge. Previous research shows that trainees garner clandestine support from faculty members who share similar values and interests. These faculty work in the shadows, assisting trainees to meet their goals of destabilizing harmful systems and structures in medical education, yet little is known about the role these faculty play. This study was designed to explore the kinds of support faculty provide, the role they play in supporting resistance efforts, and how faculty ensure their own safety. Methods: We used methodological bricolage to guide both data collection and analysis. We recruited 24 faculty from a variety of health professions and interviewed them on when they would/would not support trainees in their resistance efforts. Participants came from 12 different medical education institutions across four geographic regions of the U.S., along with one Canadian medical school. As the data came in, it was transcribed and analyzed using open coding, at which point we noticed that participants framed their roles using constructs found in the literature. Rather than continuing to open code, we refined our analysis using a deductive coding approach in which we drew on the concepts of supporters and auxiliary staff, cultural brokers, and tempered radicals. Through constant comparison, we identified patterns across participants in the roles they played and the kind of support they offered. Results: As trainees fight a metaphorical war against social harm and injustice in medical education, faculty play several key roles in supporting trainees. They protect the integrity of the institution and ensure trainees’ efforts are not disruptive to the institution’s function. They contextualize trainees’ efforts within institutional goals. They also mediate relationships between students and institutional leadership. While helping to keep themselves, trainees, and institution safe, they reinforce the importance of being a life-long resistor against social harm and injustice to continue this work. Discussion: Efforts at changing health professions education is not new; each generation gives rise to trainees who cannot bear to experience or witness the harm and injustice present in the profession’s educational and training programs and must work to change it. However, what appears to be new is that faculty are deeply engaged in this process of transformation, working alongside trainees. Given their role in the institution, they serve as the strategist in fighting this war, providing big picture opportunities and risk assessments for trainees to consider. Whereas trainees serve as the tacticians doing the work on the ground, faculty provide critical support toward the transformation of medical education.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.360
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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