Teaching Chest Tube Insertion by Blended Learning: A Multi-Dimensional Analysis
Bibliographic record
Abstract
Background Emerging technologies are being incorporated in surgical education. The use of such technology should be supported by evidence that the technology neither distracts nor overloads the learner and is easy to use. To teach chest tube insertion, we developed an e-learning module, as part of a blended learning program delivered prior to in-person hands-on simulation. This pilot study was aimed to assess learning effectiveness of this blended learning, and cognitive load and the usability of e-learning. Methods The interactive e-learning module with multimedia content was created following learning design principles. In advance of the standard simulation, 13 first-year surgical residents were randomized into two groups: 7 received the e-learning module and online reading materials (e-learning group); 6 received only the online reading materials (controls). Knowledge was evaluated by pre-and post-tests; technical performance was assessed using a Global Rating Scale by blinded assessors. Cognitive load and usability were evaluated using rating scales. Results The e-learning group showed significant improvement from baseline in knowledge ( P = .047), while controls did not ( P = .500). For technical skill, 100% of residents in the e-learning group reached a predetermined proficiency level vs 60% of controls ( P = .06). The addition of e-learning was associated with lower extrinsic and greater germane cognitive load ( P = .04, .03, respectively). Usability was evaluated highly by all participants in e-learning group. Conclusion Interactive e-learning added to hands-on simulation led to improved learning and desired cognitive load and usability. This approach should be evaluated in teaching of other procedural skills.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".