EP02.22: ISUOG 2022 international trainee survey: evaluation by N‐GEN committee of current and future ultrasound training opportunities
Bibliographic record
Abstract
To identify current and future learning needs wanted by an international community of trainees with a specific interest in ultrasound in obstetrics and gynecology (OG). An in-depth survey was designed and the ISUOG N-GEN committee performed content validation. The survey was sent to 5800 OG trainees on the ISUOG database. Closed-ended multiple choice, Likert-scale questions and open-ended questions were included. There were four sections: 1. Demographics; 2. Membership access; 3. Member/non-member experience of ultrasound training opportunities; and 4. Future training opportunities. 875 (15.1%). Responses were received. 303 (34.6%) were ISUOG members and 572 (65.4%) non-members. 45% (390/867) had performed USS for >2 years. Most participants were 25-34 years old (51.1%). The geographical location of members was widespread; the largest cohort (24.0%) from South and Central America, and the smallest (2.1%) from Australia. 89.9% reported being satisfied with ISUOG membership. A proportion were unaware of the range of ISUOG activities on offer. Topics of greatest interest were ‘first trimester fetal malformation’ and ‘fetal anomalies’. 98% were interested in ultrasound simulation training and remote real-time supervision. 31.4% have experienced ultrasound simulation training. On subgroup analysis of the non-member cohort, the most cited reason for not being a member was ‘no partnership agreement between host institution and ISUOG’ (54%; 240/447). Comparing members and non-members, a significant difference was noted regarding access to funding (p < 0.01). Ultrasound training opportunities provided by ISUOG are highly valued by participants from low and high-income countries. However, there is scope to expand the range of resources available. There is a desire to access training via simulation and real-time remote supervision. Funding is still a major issue in increasing membership numbers, especially from low-income countries.
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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 | 0.038 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".