Reducing length of stay and satisfying learner needs
Bibliographic record
Abstract
A complicated relationship exists between emergency department (ED) learner needs and patient flow with solutions to one issue often negatively affecting the other. Teaching shifts that allow clinical teachers and learners to interact without the pressure of patient care may offer a mutually beneficial solution. This study investigated the relationship between teaching shifts on ED length of stay, student self-efficacy and knowledge application.In 2012-2013, a prospective, cohort study was undertaken in a large Canadian acute-care teaching centre. All 132 clinical clerks completing their mandatory two-week emergency medicine rotation participated in three teaching shifts supervised by one faculty member without patient care responsibilities. The curriculum emphasized advanced clinical skills and included low fidelity simulation exercises, a suturing lab, image interpretation modules and discussion about psychosocial issues in emergency medicine. The clerks then completed seven clinical shifts in the traditional manner caring for patients under the supervision of an ED attending physician. Length of stay was compared during and one week following teaching shifts. A self-efficacy questionnaire was validated through exploratory factor analysis. Pre/post knowledge application was assessed using a paper-based clinical case activity.Across 40.998 patient visits, median length of stay was shortened overall by 5 minutes (95 % CI:1.2, 8.8) when clerks were involved in their teaching shifts. In the first academic block, median length of stay was reduced by 20 minutes per patient (95 % CI:12.7, 27.3). Self-efficacy showed significant improvement post teaching shifts (p < 0.001) with large effect sizes (d > 1.25) on dimensions of knowledge base, suturing, trauma and team efficacy. Students' knowledge application scores improved from pre to post (p < 0.01), with notable gains in the generation of differential diagnoses.Teaching shifts are an effective educational intervention that has a positive relation to ED patient flow while successfully attending to learner needs. Teaching shifts for the most naïve clerks in the first academic block appear to maximally benefit length of stay. Students demonstrated improved self-efficacy and knowledge application after their teaching shifts.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".