Mental Practice to Maintain Procedural Competency of Faculty with Decreased Opportunities
Bibliographic record
Abstract
Background: Physicians practicing in pediatric critical care medicine (PCCM) should maintain procedural skills competency. Faculty practicing in academic centers face challenges that may affect their procedural skills maintenance. The overall clinical opportunities are decreasing in PCCM. Faculty also have the dual role of supervising and supporting the achievement of trainee competence. Mental practice (MP) does not need direct procedural involvement and could be a helpful strategy for faculty. Objectives: This study's objective was to explore how faculty in an academic center use MP to maintain their procedural competency when faced with decreased clinical opportunities. Methods: The study was conducted in a tertiary academic center in 2023. We used a qualitative methodology using semistructured interviews as our data source. Participants were faculty practicing in PCCM. Interviews were transcribed verbatim and then analyzed and coded inductively and deductively using Guillot and Collet's Motor Imagery Integrative Model. A faculty member and a trainee performed the analysis. Differences were resolved through discussion. Triangulation was done through member checking. Results: Thirteen interviews were conducted with academic PCCM faculty. Thematic and data saturation was achieved. All faculty used MP to rehearse strategies to anticipate and troubleshoot problems. Fewer faculty members used MP to rehearse the procedural steps. MP consequently increased self-confidence and reduced anxiety. Conclusions: MP is used by faculty in performing and maintaining procedural competency. The low-resource nature of MP could make it a useful adjunct in the maintenance of procedural competency.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".