Development of An Educational Tool Using Qualitative Analysis to Teach Components of Total Laparoscopic Hysterectomy
Bibliographic record
Abstract
Background: Proficiency in total laparoscopic hysterectomy (TLH) is now a requirement of all graduating Canadian Obstetrics and Gynecology (OBGYN) trainees, but despite this requirement, a recent survey discovered that residency graduates are not receiving adequate training in TLH. Educational videos have been shown to be effective in teaching procedures in other surgical disciplines. Objective: To create an educational video to effectively teach TLH to OBGYN trainees. Methods: This qualitative study was undertaken at three academic hospitals affiliated with the University of Toronto between 2016 and 2018. Seven surgical experts were interviewed and recorded performing TLH. Qualitative and thematic analysis was used to synthesize a teaching curriculum via the Delphi method. An educational video was created and shown to small groups of consenting OBGYN trainees at various levels (15). Participants completed identical pre- and post-tests to assess knowledge, and a paired t -test was used to compare qualitative scores. A detailed focus group discussion for quality improvement was conducted. Results: The difference between the mean knowledge test scores pre-intervention (51%) compared with post-intervention (88%) was an increase of 37% (statistically significant, p -value = 0.001, CI = 2.7–4.8). Residents felt the tool was highly effective in demonstrating anatomy, surgical techniques, and clinical pearls. All residents recommended including the video in the residency curriculum. Conclusion: A TLH teaching video was systematically created, incorporating techniques from multiple surgical experts, improving gynecology trainees’ knowledge surrounding surgical technique and anatomy in a safe and convenient learning environment. Trainees would recommend this tool to their peers and recommend it be incorporated into the residency training curriculum.
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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 | 0.056 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".