Practicing Confidence: An Autoethnographic Exploration of the First Years as Physicians
Bibliographic record
Abstract
Phenomenon: Every year is heralded with a cohort of newly-minted medical school graduates. Through intense residency training and supervision, these learners gradually develop self-assurance in their newfound skills and ways of practice. What remains unknown, however, is how this confidence develops and on what it is founded. This study sought to provide an insider view of this evolution from the frontline experiences of resident doctors. Approach: Using an analytic collaborative autoethnographic approach, two resident physicians (Internal Medicine; Pediatrics) documented 73 real-time stories on their emerging sense of confidence over their first two years of residency. A thematic analysis of narrative reflections was conducted iteratively in partnership with a staff physician and a medical education researcher, allowing for rich, multi-perspective input. Reflections were analyzed and coded thematically and the various perspectives on data interpretation were negotiated by consensus discussion. Findings: In the personal stories and experiences shared, we take you through our own journey and development of confidence, which we have come to appreciate as a layered and often non-linear process. Key moments include fears in the face of the unknown; the shame of failures (real or perceived); the bits of courage gained by everyday and mundane successes; and the emergence of our personal sense of growth and physicianship. Insights: Through this work, we – as two Canadian resident physicians – have ventured to describe a longitudinal trajectory of confidence from the ground up. Although we enter residency with the label of ‘physician,’ our clinical acumen remains in its infancy. We graduate from residency still as physicians, but decidedly different in terms of our knowledge, attitudes, and skills. We sought to capitalize on the vulnerability and authenticity inherent in autoethnography to enrich our collective understanding of confidence acquisition in the resident physician and its implications for the practice of medicine.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".