What makes a good surgical experience for the naïve learner?
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Early clinical observerships play a key role in pre-clerkship education and career selection. Using a cross sectional survey design, we attempted to assess the makeup of the student's observership throughout their time in the operating room (OR). Perceived educational value (EV), utility in career exploration (CE), and level of personal enjoyment (PE) were assessed after every encounter and utilized as primary outcomes. Twenty-eight (28) 1st year medical students participating in an intensive 2-week surgical exploration program completed eight 34 question electronic surveys characterizing each of their 8 surgical observerships (224 events). One hundred forty six (65.2%) surveys were completed, each representing a day of observerships, with a total of 207 surgeries observed. Following multivariate linear regression analysis, increased surgical team engagement with the student and a positive tone of interaction were each significantly associated with improved EV (p1 = 0.013, p2 <0.001), CE (p1=0.006, p2=0.012), and PE (p1 <0.001, p2 <0.001). Surgical subspecialty, type of case and ability to scrub in were not associated with improved experiences. Increased engagement and positive interaction with the surgical team are significantly associated with various measures of improved surgical experience, and each are highly modifiable factors in a learner's OR experience. This research emphasizes the diverse educational responsibility of academic surgeons.
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 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".