Feasibility of a national Web‐based ultrasound learning portfolio for OB/Gyn residents: the KOALA program
Bibliographic record
Abstract
Purpose: To establish the feasibility of a national ultrasound learning portfolio in resident education in OB/Gyn in order to create a needs assessment for educational programming, ensure students are meeting educational objectives and promote self‐directed learning. Methods: The Internet‐based KOALA learning portfolio was introduced into 15 of 16 Canadian medical schools in 1997 to track resident learning encounters in OB/Gyn ultrasound. Data entered over 3‐year period analyzed using SPSS 10.0 to identify: (1) categories of ultrasound cases entered; (2) critical incidents of learning; (3) domain and stimulus of questions posed; and (4) educational resources used for learning. Results: Between 1 July 1997 and 30 June 2000, 3368 ultrasound encounters were voluntarily recorded by 50 residents at 11 university‐affiliated residency training programs. Twelve residents (25%) were categorized as high‐volume, having entered = 100 cases. Learning encounters were recorded according to the following themes: routine second trimester dating scans; diagnostic procedures; amniocentesis, CVS, cordocentesis; assessment of fetal well‐being; targeted anomaly screen; routine gynecological exam. Critical incidents of learning were identified in 25% of ultrasound cases (837/3368). Overall, the majority of questions asked by residents following their ultrasound learning encounter were of a cognitive nature (53.3%), and were significantly associated with identification of a critical incidence of learning (P < 0.001). Overall, medline was the educational resource most likely to be used case‐based learning when critical incidents were identified (P < 0.001). Questions of high volume users were most often stimulated by reflections and self‐assessment (81.5%) when compared to low‐volume users (61.3%). Textbooks were the educational resource most often used by high‐volume users (73.9%) to answer questions concerning their educational encounters. High‐volume users were more likely to report a change in their practice as a result of learning acquired through the use of KOALA systems (P < 0.001). Barriers identified during implementation included lack of computer Internet access in the clinical environment, suboptimal faculty development initiatives, and failure to integrate the learning portfolio as a component of the resident evaluation process. Conclusions: (1) Implementation of a Web‐based learning portfolio is feasible, allowing collection of data from geographically dispersed areas and programs; (2) voluntary use of the KOALA system was acceptable when the system was made available to OB/Gyn residents; and (3) use of Internet‐based learning portfolio such as KOALA presents a unique opportunity for ultrasound training programs and special society such as ISUOG to assess the educational needs of training physician learners.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".