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Record W4382501983 · doi:10.2196/43699

Evaluating the Effectiveness of Interactive Virtual Patients for Medical Education in Zambia: Randomized Controlled Trial

2023· article· en· W4382501983 on OpenAlexvenueno aff
R.L. Horst, Lea-Mara Witsch, Rayford Hazunga, Natasha Namuziya, Gardner Syakantu, Yusuf Ahmed, Omar Cherkaoui, Petros Andreadis, Florian Neuhann, Sandra Barteit

Bibliographic record

VenueJMIR Medical Education · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scaleRandomized controlled trialMedical educationThe InternetBachelorMedicinePower pointPsychologyComputer scienceMathematics educationInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Zambia is facing a severe shortage of health care workers, particularly in rural areas. Innovative educational programs and infrastructure have been established to bridge this gap; however, they encounter substantial challenges because of constraints in physical and human resources. In response to these shortcomings, strategies such as web-based and blended learning approaches have been implemented, using virtual patients (VPs) as a means to promote interactive learning at the Levy Mwanawasa Medical University (LMMU) in Zambia. OBJECTIVE: This study aimed to evaluate the students' knowledge acquisition and acceptance of 2 VP medical topics as a learning tool on a Zambian higher education e-learning platform. METHODS: Using a mixed methods design, we assessed knowledge acquisition using pre- and posttests. In a randomized controlled trial setting, students were assigned (1:1) to 2 medical topics (topic 1: appendicitis and topic 2: severe acute malnutrition) and then to 4 different learning tools within their respective exposure groups: VPs, textbook content, preselected e-learning materials, and self-guided internet materials. Acceptance was evaluated using a 15-item questionnaire with a 5-point Likert scale. RESULTS: A total of 63 third- and fourth-year Bachelor of Science clinical science students participated in the study. In the severe acute malnutrition-focused group, participants demonstrated a significant increase in knowledge within the textbook group (P=.01) and the VP group (P=.01). No substantial knowledge gain was observed in the e-learning group or the self-guided internet group. For the appendicitis-focused group, no statistically significant difference in knowledge acquisition was detected among the 4 intervention groups (P=.62). The acceptance of learning materials exhibited no substantial difference between the VP medical topics and other learning materials. CONCLUSIONS: In the context of LMMU, our study found that VPs were well accepted and noninferior to traditional teaching methods. VPs have the potential to serve as an engaging learning resource and can be integrated into blended learning approaches at LMMU. However, further research is required to investigate the long-term knowledge gain and the acceptance and effectiveness of VPs in medical education. TRIAL REGISTRATION: Pan African Clinical Trials Registry (PACTR) PACTR202211594568574; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=20413.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized triallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized trialhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.015
metaresearch head score (Gemma)0.128
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.128
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.452
Teacher spread0.435 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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
Published2023
Admission routes1
Has abstractyes

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