Knowledge acquisition and retention following Saving Children’s Lives course for healthcare providers in Botswana: a longitudinal cohort study
Bibliographic record
Abstract
OBJECTIVES: Millions of children die every year from serious childhood illnesses. Most deaths are avertable with access to quality care. Saving Children's Lives (SCL) includes an abbreviated high-intensity training (SCL-aHIT) for providers who treat serious childhood illnesses. The objective of this study was to examine the impact of SCL-aHIT on knowledge acquisition and retention of providers. SETTING: 76 participating centres who provide primary and secondary care in Kweneng District, Botswana. PARTICIPANTS: Doctors and nurses expected by the District Health Management Team to provide initial care to seriously ill children, completed SCL-aHIT between January 2014 and December 2016, submitted demographic data, course characteristics and at least one knowledge assessment. METHODS: Retrospective, cohort study. Planned and actual primary outcome was adjusted acquisition (change in total knowledge score immediately after training) and retention (change in score at 1, 3 and 6 months), secondary outcomes were pneumonia and dehydration subscores. Descriptive statistics and linear mixed models with random intercept and slope were conducted. Relevant institutional review boards approved this study. RESULTS: 211 providers had data for analysis. Cohort was 91% nurses, 61% clinic/health postbased and 45% pretrained in Integrated Management of Childhood Illness (IMCI). A strong effect of SCL-aHIT was seen with knowledge acquisition (+24.56±1.94, p<0.0001), and loss of retention was observed (-1.60±0.67/month, p=0.018). IMCI training demonstrated no significant effect on acquisition (+3.58±2.84, p=0.211 or retention (+0.20±0.91/month, p=0.824) of knowledge. On average, nurses scored lower than physicians (-19.39±3.30, p<0.0001). Lost to follow-up had a significant impact on knowledge retention (-3.03±0.88/month, p=0.0007). CONCLUSIONS: aHIT for care of the seriously ill child significantly increased provider knowledge and loss of knowledge occurred over time. IMCI training did not significantly impact overall knowledge acquisition nor retention, while professional status impacted overall score and lost to follow-up impacted retention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".