Navigating the paradox: Exploring resident experiences of vulnerability
Bibliographic record
Abstract
INTRODUCTION: Learning and growth in postgraduate medical education (PGME) often require vulnerability, defined as a state of openness to uncertainty, risk, and emotional exposure. However, vulnerability can threaten a resident's credibility and professional identity. Despite this tension, studies examining vulnerability in PGME are limited. As such, this study aims to explore residents' experiences of vulnerability, including the factors that influence vulnerability in PGME. METHODS: Using a constructivist grounded theory approach, individual semi-structured interviews were conducted with 15 residents from 10 different specialities. Interview transcripts were coded and analysed iteratively. Themes were identified and relationships among themes were examined to develop a theory describing vulnerability in PGME. RESULTS: Residents characterised vulnerability as a paradox represented by two overarching themes. 'Experiencing the tensions of vulnerability' explores the polarities between being a fallible, authentic learner and an infallible, competent professional. 'Navigating the vulnerability paradox' outlines the factors influencing the experience of vulnerability and its associated outcomes at the intrapersonal, interpersonal, and systems levels. Residents described needing to have the bandwidth to face the risks and emotional labour of vulnerability. Opportunities to build connections with social agents, including clinical teachers and peers, facilitated vulnerability. The sociocultural context shaped both the experience and outcomes of vulnerability as residents faced the symbolic mask of professionalism. CONCLUSION: Residents experience vulnerability as a paradox shaped by intrapersonal, interpersonal, and systems level factors. These findings capture the nuance and complexity of vulnerability in PGME and offer insight into creating supportive learning environments that leverage the benefits of vulnerability while acknowledging its risks. There is a need to translate this understanding into systems-based change to create supportive PGME environments, which value and celebrate vulnerability.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".