Motivation to access laparoscopic skills training: Results of a national survey of obstetric and gynaecology residents
Bibliographic record
Abstract
Objective Competency based medical education (CBME) requires novel approaches to surgical education. Significant investment has been made in laparoscopic simulation, which has been shown to foster skill development prior to patient encounters. However, research suggests variable voluntary use of these resources by residents, and little is known about the motivational factors that influence their utilization. The purpose of this study was to characterize factors that motivate residents to seek laparoscopic simulation experience outside of the formal curriculum. Design We developed a questionnaire grounded in Expectancy Value Theory, an established psychological theory of motivation, by adapting validated measures to fit the study context. We conducted a cross sectional survey of Canadian obstetrics and gynecology residents. Setting We invited residents enrolled in English-language obstetrics and gynecology training programs in Canada to participate. Participants All residents engaged in clinical duties during Winter 2018 were invited to complete the questionnaire. Forty-four Obstetrics and Gynecology (Ob/Gyn) residents participated in the study. Results Residents reported limited use of simulation resources and identified multiple barriers including lack of time, access, and supervision. They expressed concern about development of bad habits during independent practice, and simulation use was positively correlated with perceived utility. Compared to junior residents, senior residents reported greater enjoyment of laparoscopic surgery, less emotional costs, and higher self-efficacy for learning laparoscopy. Conclusions Residents’ perception of utility and barriers impede voluntary simulation use and overall use was limited. As programs undertake curricula redevelopment for CBME, mitigating barriers and improving perceived utility of laparoscopic simulation is vital to increase use and enhance skill development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".