Utilization of a novel mobile application, “HBB Prompt”, to reduce Helping Babies Breathe skills decay
Bibliographic record
Abstract
BACKGROUND: Helping Babies Breathe (HBB) is a newborn resuscitation training program designed to reduce neonatal mortality in low- and middle-income countries. However, skills decay after initial training is a significant barrier to sustained impact. OBJECTIVE: To test whether a mobile app, HBB Prompt, developed with user-centred design, helps improve skills and knowledge retention after HBB training. METHODS: HBB Prompt was created during Phase 1 of this study with input from HBB facilitators and providers from Southwestern Uganda recruited from a national HBB provider registry. During Phase 2, healthcare workers (HCWs) in two community hospitals received HBB training. One hospital was randomly assigned as the intervention hospital, where trained HCWs had access to HBB Prompt, and the other served as control without HBB Prompt (NCT03577054). Participants were evaluated using the HBB 2.0 knowledge check and Objective Structured Clinical Exam, version B (OSCE B) immediately before and after training, and 6 months post-training. The primary outcome was difference in OSCE B scores immediately after training and 6 months post-training. RESULTS: Twenty-nine HCWs were trained in HBB (17 in intervention, 12 in control). At 6 months, 10 HCW were evaluated in intervention and 7 in control. In intervention and control respectively, the median OSCE B scores were: 7 vs. 9 immediately before training, 17 vs. 21 immediately after training, and 12 vs. 13 at 6 months after training. Six months after training, the median difference in OSCE B scores was -3 (IQR -5 to -1) in intervention and -8 (IQR -11 to -6) in control (p = 0.02). CONCLUSION: HBB Prompt, a mobile app created by user-centred design, improved retention of HBB skills at 6 months. However, skills decay remained high 6 months after training. Continued adaptation of HBB Prompt may further improve maintenance of HBB skills.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".