Helping babies breathe: assessing the effectiveness of simulation-based high-frequency recurring training in a community-based setting of Pakistan
Bibliographic record
Abstract
BACKGROUND: Birth asphyxia is one of the significant causes of neonatal deaths in Pakistan. Poor newborn resuscitation skills of birth attendants are a major cause of neonatal mortality in low resource settings across the globe. This study aimed to evaluate the effectiveness of the Simulation-Based High-Frequency training of the Helping Babies Breathe for Community Midwives (CMW), in district Gujrat, Pakistan. METHOD: A pre-post-test interventional study design was used. The universal sampling technique was employed to recruit 50 deployed CMWs in the entire district of Gujrat. The pre-tested module and tools of Helping Babies Breathe (2nd edition) were used in the intervention. Using the High Frequency training approach, three one-day training sessions were conducted for CMWs at an interval of 2 months. During the 2 months interval, participants were monitored and supported to practice their skills at their birthing centers. Knowledge and skills were assessed before and after each session. The McNemar and Cochran's Q tests were applied for data analysis. Participants' feedback was also obtained at the end of each training, which was analyzed through descriptive statistics. RESULTS: Data from 34 CMWs were analyzed as they completed all three training sessions and assessments. The results were statistically different after each training session for OSCE B (p-value < 0.05). However, for knowledge and OSCE A, significant improvement was observed after training sessions 1 and 2 only. Pairwise comparison showed that pre-assessment at training 1 was significantly different from most of the repeated measures of knowledge, OSCE A, and OSCE B. Moreover, the learners appreciated the overall training in terms of organization, content, material, assessment, and overall competency. Additionally, due to a small sample size of the CMWs, and a short time of the intervention, significant differences in morbidity and mortality outcomes could not be detected. CONCLUSION: The study concluded that a series of training and continuous supportive supervision and facilitation enhances Helping Babies Breathe (HBB) knowledge retention and skills. The study recommends, periodic, structured and precise HBB trainings, with ongoing quality monitoring activities through blended learning modalities would help sustain and scale-up the intervention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".