An evaluation of cascading mentorship as advocacy training in undergraduate medical education
Bibliographic record
Abstract
BACKGROUND: Physicians are in a position of great influence to advocate for health equity. As such, it is important for physicians-in-training to develop the knowledge and skills necessary to fulfil this role. Although various undergraduate medical programs have implemented health advocacy training, they often lack experiential learning and physician involvement. These aspects are foundational to the Advocacy Mentorship Initiative (AMI) which utilizes cascading mentorship as a novel approach to advocacy training. Medical students develop advocacy competency as peer mentors to youth raised in at-risk environments, while also being mentored themselves by physician residents. We aim to determine whether there are specific advantages to utilizing cascading mentorship to facilitate the attainment of advocacy competencies in undergraduate medical education. METHODS: Medical students participating in AMI between 2017 to 2020 completed pre- and post-exposure questionnaires. Questionnaires assessed confidence in advocacy-related skills and knowledge of youth advocacy concepts, as well as learning goals, skills gained, benefits of AMI and resident mentors, and impact on future career. Sign tests were utilized to analyze quantitative results, and content analysis was used for open-ended responses. A triangulation protocol was also utilized. RESULTS: Fifty mentors participated, 24 (48%) of which completed both pre- and post-exposure questionnaires. Participants gained confidence in advocacy-related skills (p < 0.05) such as working with vulnerable populations and advocating for medical and non-medical needs. They also reported significant improvements (p < 0.01) in their understanding of social determinants of health and concepts related to children's health and development. Content analysis showed that participants built meaningful relationships with mentees in which they learned about social determinants of health, youth advocacy, and developed various advocacy-related skills. Participants greatly valued mentorship by residents, identifying benefits such as support and advice regarding relations with at-risk youth, and career mentorship. AMI impacted participants' career trajectories in terms of interest in working with youth, psychiatry, and advocacy. CONCLUSIONS: AMI offers a unique method of advocacy training through cascading mentorship that engages medical students both as mentors to at-risk youth and mentees to resident physicians. Through cascading mentorship, medical students advance in their advocacy-related skills and understanding of social determinants of health.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".