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Record W2991154712 · doi:10.2196/15828

The mPOWERED Electronic Learning System for Intimate Partner Violence Education: Mixed Methods Usability Study

2019· article· en· W2991154712 on OpenAlexvenueno aff
Charmayne Hughes, Elaine Musselman, Lilia Walsh, Tatiana Mariscal, Sam Warner, Amy Hintze, Neela Rashidi, Chloe Gordon-Murer, Tiana Tanha, Fahrial Licudo, Rachel Ng, Jenna Tran

Bibliographic record

VenueJMIR Nursing · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilitySystem usability scalePluralistic walkthroughDomestic violenceNursingHealth carePsychologyMedical educationWeb usabilityMedicinePoison controlHuman factors and ergonomicsMedical emergencyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.430
Teacher spread0.408 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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