The mPOWERED Electronic Learning System for Intimate Partner Violence Education: Mixed Methods Usability Study
Bibliographic record
Abstract
BACKGROUND: Nurse practitioners are a common resource for victims of intimate partner violence (IPV) presenting to health care settings. However, they often have inadequate knowledge about IPV and lack self-efficacy and confidence to be able to screen for IPV and communicate effectively with patients. OBJECTIVE: The aim of this study was to develop and test the usability of a blended learning system aimed at educating nurse practitioner students on topics related to IPV (ie, the mPOWERED system [Health Equity Institute]). METHODS: Development of the mPOWERED system involved usability testing with 7 nurse educators (NEs) and 18 nurse practitioner students. Users were asked to complete usability testing using a speak-aloud procedure and then complete a satisfaction and usability questionnaire. RESULTS: Overall, the mPOWERED system was deemed to have high usability and was positively evaluated by both NEs and nurse practitioner students. Respondents provided critical feedback that will be used to improve the system. CONCLUSIONS: By including target end users in the design and evaluation of the mPOWERED system, we have developed a blended IPV learning system that can easily be integrated into health care education. Larger-scale evaluation of the pedagogical impact of this system is underway.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".