Personal career decisions during medical training are not complicated, they are complex
Bibliographic record
Abstract
BACKGROUND: For medical training to be deemed successful, in addition to gaining the skills required to make appropriate clinical decisions, trainees must learn how to make good personal decisions. These decisions may affect satisfaction with career choice, work-life balance, and their ability to maintain/improve clinical performance over time-outcomes that can impact future wellness. Here, the authors introduce a decision-making framework with the goal of improving our understanding of personal decisions. METHODS: Stemming from the business world, the Cynefin framework describes five decision-making domains: clear, complicated, complex, chaotic, and confusion, and a key inference of this framework is that decision-making can be improved by first identifying the decision-making domain. Personal decisions are largely complex-so applying linear decision-making strategies is unlikely to help in this domain. RESULTS: The available data suggest that the outcomes of personal decisions are suboptimal, and the authors propose three mechanisms to explain these findings: (1) Complex decision is susceptible to attribute substitution where we subconsciously trade these decisions for easier decisions; (2) predictions are prone to cognitive biases, such as assuming our situation will remain constant (linear projection fallacy), believing that accomplishing a goal will deliver lasting happiness (arrival bias), or overestimating benefits and underestimating costs of future tasks (planning fallacy); and (3) complex decisions have an inherently higher failure rate than complicated decisions because they are the result of an ongoing, dynamic person-by-situation interaction and, as such, have more time to fail and more ways to do so. DISCUSSION: Based upon their view that personal decisions are complex, the authors propose strategies to improve satisfaction with personal decisions, including increasing awareness of biases that may impact personal decisions. Recognising that the outcome of personal decisions can change over time, they also suggest additional interventions to manage these decisions, such as different forms of mentoring.
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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 | 0.010 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".