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.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".