Shifting and Sharing: Academic Physicians’ Strategies for Navigating Underperformance and Failure
Bibliographic record
Abstract
PURPOSE: Medical practice is uncertain and complex. Consequently, even outstanding performers will inevitably experience moments of underperformance and failure. Coping relies on insight and resilience. However, how physicians develop and use these skills to navigate struggle remains underexplored. A better understanding may reveal strategies to support both struggling learners and stressed practitioners. METHOD: In 2015, 28 academic physicians were interviewed about their experiences with underperformance or failure. Constructivist grounded theory informed data collection and analysis. RESULTS: Participants' experiences with struggle ranged from patient errors and academic failures to frequent, smaller moments of interpersonal conflict and work-life imbalance. To buffer impact, participants sometimes shifted their focus to an aspect of their identity where they felt successful. Additionally, although participants perceived that insight develops by acknowledging and reflecting on error, they sometimes deflected blame for performance gaps. More often, participants seemed to accept personal responsibility while simultaneously sharing accountability for underperformance or failure with external forces. Paradoxically, participants perceived learners who used these strategies as lacking in insight. CONCLUSIONS: Participants demonstrated the protective and functional value of distributing responsibility for underperformance and failure. Shifting and sharing may be an element of reflection and resilience; recognizing external factors may provide a way to gain perspective and to preserve the self. However, this strategy challenges educators' assumptions that learners who deflect are avoiding personal responsibility. The authors' findings raise questions about what it means to be resilient, and how assumptions about learners' responses to failure may affect strategies to support underperforming learners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".