Overshadowed by shadowing: exploring how Canadian medical students experience shadowing
Bibliographic record
Abstract
Background: Preclinical medical students commonly perceive shadowing as beneficial for career exploration. However, research is sparse on the broader impact of shadowing as a learning strategy. We explored students' perceptions and lived experiences of shadowing to understand its role and impact on their personal and professional lives. Methods: Between 2020-2021, individual semi-structured video interviews were conducted with 15 Canadian medical students in this qualitative descriptive study. Inductive analysis proceeded concurrently with data collection until no new dominant concepts were identified. Data were iteratively coded and grouped into themes. Results: Participants described internal and external factors that moulded shadowing experiences, arising tensions between intended and perceived experiences, and how these lived experiences impacted their wellness. Internal factors associated with shadowing behaviour included: 1) aspiring to be the best and shadowing to demonstrate excellence, 2) shadowing for career exploration, 3) shadowing as learning opportunities for early clinical exposure and career preparedness, and 4) reaffirming and redefining professional identity through shadowing. External factors were: 1) unclear residency match processes which position shadowing as competitive leverage, 2) faculty messaging that perpetuates student confusion around the intended value of shadowing, and 3) social comparison in peer discourse, fuelling a competitive shadowing culture. Conclusions: The tension between balancing wellness with career ambitions and the unintended consequences of unclear messaging regarding shadowing in a competitive medical culture highlights issues inherent in shadowing culture.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".