Mixed-method study to assess the feasibility, acceptability and early effectiveness of the Hospital to Home programme for follow-up of high-risk newborns in a rural district of Central Uganda: a study protocol
Bibliographic record
Abstract
INTRODUCTION: A follow-up programme designed for high-risk newborns discharged from inpatient newborn units in low-resource settings is imperative to ensure these newborns receive the healthiest possible start to life. We aim to assess the feasibility, acceptability and early outcomes of a discharge and follow-up programme, called Hospital to Home (H2H), in a neonatal unit in central Uganda. METHODS AND ANALYSIS: We will use a mixed-methods study design comparing a historical cohort and an intervention cohort of newborns and their caregivers admitted to a neonatal unit in Uganda. The study design includes two main components. The first component includes qualitative interviews (n=60 or until reaching saturation) with caregivers, community health workers called Village Health Team (VHT) members and neonatal unit staff. The second component assesses and compares outcomes between a prospective intervention cohort (n=100, born between July 2019 and September 2019) and a historical cohort (n=100, born between July 2018 and September 2018) of infants. The historical cohort will receive standard care while the intervention cohort will receive standard care plus the H2H intervention. The H2H intervention comprises training for healthcare workers on lactation, breast feeding and neurodevelopmentally supportive care, including cue-based feeding, and training to caregivers on recognition of danger signs and care of their high-risk infants. Infants and their families receive home visits until 6 months of age, or longer if necessary, by specially trained VHTs. Quantitative data will be analysed using descriptive statistics and regression analysis. All results will be stratified by cohort group. Qualitative data will be analysed guided by Braun and Clarke's thematic analysis technique. ETHICS AND DISSEMINATION: This study protocol was approved by the relevant Ugandan ethics committees. All participants will provide written informed consent. We will disseminate through peer-reviewed publications and key stakeholders and public engagement. TRIAL REGISTRATION NUMBER: ISRCTN51636372; Pre-result.
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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 | 0.083 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".