Mental Practice as an Additional Step Before Simulation Practice Facilitates Training in Bronchoscopic Intubation
Bibliographic record
Abstract
BACKGROUND: Learning bronchoscopy is challenging for novices, as it requires navigation in a 3-dimensional space under 2-dimensional viewing conditions and execution of complex motor skills with an unfamiliar instrument. Mental practice exercises are based on repeated visualization of motor actions without physically performing them, thereby promoting the learning of skills. We aimed to evaluate whether a teaching intervention including mental practice exercise modules for the acquisition of bronchoscopy skills improves fiberoptic intubation performance of novice learners. METHODS: In this prospective cohort study, 24 pediatric intensive care trainees and respiratory therapists participating in a bronchoscopy learning curriculum in 2016-2017 attended a theoretical lecture followed by self-guided learning. Subsequently, the learners were randomly assigned to either participating in a teaching intervention including mental practice exercises or not (control group). The primary outcome was time to complete their first bronchoscopic intubation using a virtual reality simulator. Secondary outcomes were the occurrence of "red outs" (ie, the anatomy could no longer be visualized) or collisions with the airway wall. Bayesian Poisson Mixture models were used to estimate the effect of the intervention on outcomes. Furthermore, participation in the teaching intervention was examined in short interviews and with descriptive thematic analysis. RESULTS: Subjects in the intervention group completed the bronchoscopy on average 1.2 times faster (rate ratio 1.2 [95% credible intervals 1.1-1.3]). The posterior probability that the teaching intervention reduced the occurrence of "red outs" by more than half was 86%. No differences were found regarding the odds of colliding with the airway wall. Everyone except 1 trainee in the mental practice group engaged with and found the mental practice modules helpful. CONCLUSIONS: A teaching intervention including mental practice exercises represents a valuable additional learning strategy promoting the performance and complex skill acquisition of novice learners in the initial stages of learning bronchoscopy procedures.
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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".