Designing and evaluating a novel mobile application for Helping Babies Breathe skills retention in Uganda: comparative study protocol
Bibliographic record
Abstract
INTRODUCTION: Over 600 000 newborns die each year of intrapartum-related events, many of which are preventable in the presence of skilled birth attendants. Helping Babies Breathe (HBB) is a neonatal resuscitation training programme designed for low-resource settings that can reduce both early neonatal mortality and stillbirths. However, as in other similar educational programmes, knowledge and skill retention deteriorate over time. This trend may be counteracted by strategies such as regular simulated exercises. In this study, a mobile application (app) 'HBB Prompt' will be developed to assist providers in retaining HBB knowledge and skills. METHODS AND ANALYSIS: This is a comparative study in Uganda with two phases: an app development phase and an assessment phase. In the first phase, HBB trainers and providers will explore barriers and facilitators to enhance learning and maintenance of HBB skills and knowledge through focus group discussions (FGDs). The FGDs are designed with a human factors perspective, enabling collection of relevant data for the prototype version of HBB Prompt. The app will then undergo usability and feasibility testing through FGDs and simulations. In the second phase, a minimum of 10 healthcare workers from two district hospitals will receive HBB training. Only the intervention hospital will have access to HBB Prompt. All participants will be asked to practise HBB skills every shift and record this in a logbook. In the intervention site, app usage data will also be collected. The primary outcome will be comparing skills retention 12 months after training, as determined by Objective Structured Clinical Examination B scores. ETHICS AND DISSEMINATION: This study received ethics approval from The Hospital for Sick Children and Mbarara University of Science and Technology. The authors plan to publish all relevant findings from this study in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT03577054.
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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 | 0.042 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".