Examining Residents' Strategic Mindfulness During Self-Regulated Learning of a Simulated Procedural Skill
Bibliographic record
Abstract
BACKGROUND: Simulation-based training is currently embedded in most health professions education curricula. Without evidence for how trainees think about their simulation-based learning, some training techniques may not support trainees' learning strategies. OBJECTIVE: This study explored how residents think about and self-regulate learning during a lumbar puncture (LP) training session using a simulator. METHODS: In 2010, 20 of 45 postgraduate year 1 internal medicine residents attended a mandatory procedural skills training boot camp. Independently, residents practiced the entire LP skill on a part-task trainer using a clinical LP tray and proper sterile technique. We interviewed participants regarding how they thought about and monitored their learning processes, and then we conducted a thematic analysis of the interview data. RESULTS: The analysis suggested that participants considered what they could and could not learn from the simulator; they developed their self-confidence by familiarizing themselves with the LP equipment and repeating the LP algorithmic steps. Participants articulated an idiosyncratic model of learning they used to interpret the challenges and successes they experienced. Participants reported focusing on obtaining cerebrospinal fluid and memorizing the "routine" version of the LP procedure. They did not report much thinking about their learning strategies (eg, self-questioning). CONCLUSIONS: During simulation-based training, residents described assigning greater weight to achieving procedural outcomes and tended to think that the simulated task provided them with routine, generalizable skills. Over this typical 1-hour session, trainees did not appear to consider their strategic mindfulness (ie, awareness and use of learning strategies).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".