Development of equity, diversity, and inclusion competencies in residents and faculty in oncology through formal and informal learning.
Bibliographic record
Abstract
9053 Background: In recent years, a growing body of literature has suggested that patients need their clinicians to provide culturally competent care. A focus on integrated and longitudinal training within the domains of equity, diversity and inclusion (EDI) is needed to equip physicians to meet patients’ needs. Oncology is one such specialty that needs strong skillsets in EDI given its vulnerable and complex patient populations. This study explores how physicians within oncology learn about the domains of EDI through formal and informal learning. Methods: Using constructivist grounded theory (CGT), this study explores EDI competency formation at one academic center – the Juravinski Cancer Center in Hamilton ON, Canada. A purposive sample of 16 staff and resident physicians was taken to incorporate variation sampling - including a variety of ages, genders, and work/training experience. Participants were from both medical and radiation oncology. Semi-structured one-on-one interviews were conducted. Transcripts were generated, anonymized, and analyzed iteratively. Data analysis followed stages of open, axial, and selective coding through which themes were constructed. Interviews were continued until data saturation was reached. Results: Of the 16 participants, there was an even distribution between men (8) and women (8). Mean age was 43 (range 30-65). There were 5 residents and 11 faculty members. 9 were from medical oncology and 7 from radiation oncology. The major themes generated from the study were: the relationship between EDI competencies and professional identify formation, the role of culture and context in influencing exposure and learning about EDI, and the relationship between formal and informal learning opportunities. Conclusions: This study is the first to explore of how oncologists presently develop EDI competencies through formal and informal learning. The study has discovered the role of professional identify formation as a factor influencing learning, the impact of the culture and context of medicine, and the significant interplay between formal and informal learning in developing EDI skillsets. While much learning takes place informally, informed by clinical encounters and personal experiences, there is a need to marry the informal learning opportunities to more structured formal teaching in the training and clinical environment. [Table: see text]
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".