Determinants of research engagement in academic obstetrics and gynaecology
Bibliographic record
Abstract
BACKGROUND: To identify the determinants of research engagement among faculty in an academic department of Obstetrics and Gynaecology. METHODS: All members of the Department of Obstetrics and Gynaecology at the University of British Columbia were mailed an online version of the Edmonton Research Orientation Survey (EROS) in 2011 and in 2014. High scores on overall research engagement and on each of the 4 subscales, namely, value of research, value of innovation, research involvement and research utilization/evidence-based practice were quantified. Analyses were carried out on both surveys combined and on the 2014 survey separately. Logistic regression was used to identify determinants of high levels of research engagement. RESULTS: The overall response rate was 37% (130 responses; 54 respondents in 2011 and 76 respondents in 2014). The average EROS score was 140 (range 54 to 184) and 35% of respondents had a score ≥150. Significant determinants of positive research engagement based on the overall EROS scale included being paid for research work (adjusted odds ratio [AOR] 22.1, 95% confidence interval [CI] 2.47-197.7) and carrying out research during unpaid hours (AOR 6.41, 95% CI 1.97-20.9). Age <50 years (AOR 11.0, 95% CI 1.35-89.9) and clinical experience <20 years (AOR 19.7, 95% CI 2.18-178.8) were positively associated, while journal reading during unpaid hours (AOR 0.21, 95% CI 0.07-0.62) was negatively associated with specific EROS subscales. CONCLUSIONS: In a setting with a positive research orientation, research engagement among the faculty was associated with paid research time, research work and journal reading during unpaid hours and more recent entry into clinical practice.
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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 | 0.011 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".