Trainee selection of tasks in postgraduate medical education: Is there a role for ‘cherry‐picking’ to optimise learning?
Bibliographic record
Abstract
PURPOSE: Learning is optimised when postgraduate trainees engage in clinical tasks in their zone of proximal development (ZPD). However, workplace learning environments impose additional non-learning goals and additional tasks that may lead to trainees engaging in tasks that do not fall within their ZPD. We do not fully understand how trainees select clinical tasks in the workplace learning environment. If we knew the goals and factors they consider when selecting a task, we could better equip trainees with strategies to select tasks that maximise learning. We explored how postgraduate trainees select clinical tasks using echocardiography interpretation as a model. METHODS: Canadian General Cardiology residents and Echocardiography fellows were invited to participate in semi-structured interviews. Aligning with a theory-informed study, two independent researchers used a deductive, directed content analysis approach to identify codes and themes. RESULTS: Eleven trainees from seven Canadian universities participated (PGY4 = 4, PGY5 = 3, PGY6 = 1 and echocardiography fellows = 3). Goals included learning content, fulfilling assessment criteria and contributing to clinical demands. Trainees switched between goals throughout the day, as it was too effortful for them to engage in tasks within their ZPD at all times. When trainees had sufficient mental effort available, they selected higher complexity tasks that could advance learning content. When available mental effort was low, trainees selected less complex tasks that fulfilled numerically based assessment goals or contributed to clinical demands. Trainees predominantly used perceived complexity of the echocardiogram as a factor to select tasks to achieve their desired goals. CONCLUSION: Postgraduate trainees select tasks within their ZPD that enable them to maximise learning when they perceive to have sufficient mental effort available and workplace affordances are adequate. These findings can inform individual and systemic strategies to maximise learning when selecting tasks.
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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 | 0.037 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".