Assessing Knowledge, Competence, and Performance Following Web-Based Education on Early Breast Cancer Management: Health Care Professional Questionnaire Study and Anonymized Patient Records Analysis
Bibliographic record
Abstract
BACKGROUND: Web-based learning activities are key components of continuing medical education (CME) for health care professionals (HCPs). However, the published outcomes of web-based educational interventions for early breast cancer (EBC) are limited. OBJECTIVE: This study aims to objectively assess knowledge, competence, and performance among HCPs following participation in 2 EBC-focused CME activities and to identify the remaining educational gaps. METHODS: We developed 2 CME-accredited web-based educational activities addressing high-risk EBC, including integration of shared decision-making to optimize patient care (touchMDT) and stratification for early identification of high-risk patients and novel treatment strategies (touchPANEL DISCUSSION). Knowledge, competence, and performance were assessed before and after the activities against an expanded outcomes framework (levels 1-5) using self-reported questionnaires and an analysis of anonymized data extracted from patient records. RESULTS: Six months after the launch of the activity, 7047 and 8989 HCP participants engaged with touchMDT and touchPANEL DISCUSSION, respectively. The overall satisfaction was 82% (a total score of 20.6 out of 25) for the touchMDT and 88% (a total score of 21.9 out of 25) for the touchPANEL DISCUSSION. For the evaluation of knowledge and competence (50 respondents before the activity and 50 learners after the activity), there was a significant increase in the mean number of correctly answered questions from pre- to postactivity (touchMDT: median 4.0, IQR 3.0-5.0 to median 5.5, IQR 4.0-7.0; mean 4.00, SD 1.39 to mean 5.30, SD 1.56 and touchPANEL DISCUSSION: median 4.0, IQR 4.0-5.0 to median 6.0, IQR 5.0-7.0; mean 4.32, SD 1.30 to mean 5.88, SD 1.49; both P<.001). A significant improvement in self-reported performance (50 respondents before the activity and 50 learners after the activity) was observed in a combined analysis of both activities (median 3.0, IQR 2.0-3.0 to median 4.0, IQR 3.0-5.0; mean 2.82, SD 1.08 to mean 4.16, SD 1.45; P<.001). Patient record analysis (50 respondents before the activity and 50 learners after the activity) showed that the HCPs used a range of measures to determine EBC recurrence risk and revealed no significant differences in adjuvant therapies used before and after the activity (P=.97 and P>.99 for Ki-67 <20% and Ki-67 ≥20% tumors, respectively). The remaining educational gaps included strategies for implementing shared decision-making in clinical practice and the use of genetic and biomarker testing to guide treatment selection. CONCLUSIONS: Brief, web-based CME activities on EBC were associated with an improvement in HCP knowledge, competence, and self-reported performance and can help identify unmet needs to inform the design of future CME activities.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".