Digital vs. conventional instructor-led midwifery training in Benue State, Nigeria: a randomized non-inferiority trial
Bibliographic record
Abstract
Background Many health education institutions in African countries such as Nigeria face increasing enrolment of students but lack an adequate number of instructors to train them. If digital learning can be demonstrated effective in augmenting knowledge and skills, this approach could help fill this gap and improve education efficiency. A needs assessment in two Nigerian midwifery schools confirmed that digital learning would be feasible and welcomed. In this study, the Midwifery Active Digitization Empowerment Initiative (MADE-I) program was tested to determine if digital delivery of the Fundamental Interventions, Referral and Safe Transfer (FIRST) course is at least equally effective for training midwifery students compared to conventional small-group delivery. Methods A non-inferiority randomized controlled trial design was used, enrolling 130 s-year students from 2 midwifery schools in Benue State, Nigeria. Students were randomly assigned into six cohorts. Each cohort received half of the course on a Learning Management Platform on their mobile phones, the other half through standard small-group teaching. Students’ knowledge, thinking, and technical skills were assessed using a pre-test, post-test, Objective Structured Clinical Exam (OSCE), and daily modular quizzes. The data was analyzed using the difference-in-difference method. Results The study revealed that post-intervention student knowledge and thinking skills did not significantly differ between digital learning (75.26%) and small-group learning arms of the trial (75.02%, p = 0.404). Student knowledge improved significantly compared to the pre-test in both groups (by 25.03 points in the digital arm, 26.39, in small-group). Some differences were observed between digital and small-group learning in disaggregated analysis by specific module and midwifery school. Although there was a trend toward small-group learning of technical skills being more effective than digital learning, no significant differences between groups were observed in the post-intervention OSCE. Students in both groups learned equally well regardless of age, gender, and midwifery school entrance exam score. Conclusion Digital learning is as effective as small-group learning, for midwifery trainees, in augmenting knowledge, thinking, and technical skills addressed in the FIRST course, and have lighter human resource requirements, an important consideration especially in LMIC. However, similar assessments would be needed to assess effectiveness for other digitally delivered clinical education programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".