Early surgical exposure for medical students: Efficacy and effect on choice of electives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: The number of students applying to surgical residency programs is declining. The reasons are multi- factorial, however early exposure has been shown to increase application rates and decrease residency attrition rates. The objective of this study is to evaluate the Surgical Exploration and Discovery (SEAD) program, an early surgical exposure program, on its efficacy and influence on medical school electives. Methods: Two online surveys were distributed to participants of the SEAD program from 2016-2017.The surveys addressed demographics and prior surgical exposure, efficacy of the program, and the role of SEAD on influencing choice of electives.The Likert scale was used to measure responses along with multiple-choice questions. Univariate descriptive statistics were completed on all variables. Results: All participants (n = 36, 100% response rate) reported that SEAD made them more likely to enter a surgical career (Mean: 4.1 out of 5, SD: 0.8), helped narrow down career options (Mean: 4.0, SD: 0.9), and improved comfort in the OR environment (Mean: 4.7, SD; 0.5).The majority of students were planning to, or had completed at least one surgical elective in second year (72.2%) and felt that the program will influence their choice of electives in fourth year (Mean: 4.0, SD: 0.6). Conclusion:The SEAD program is an effective method to help students make career decisions, offer early surgical exposure, and help with choice of medical electives.With a lack of early surgical exposure, and declining interest in surgical programs the SEAD program is a valuable addition to medical school education.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it