A study to evaluate the effectiveness of Best Beginnings’ Baby Buddy phone app in England: a protocol paper
Bibliographic record
Abstract
IntroductionDevelopments in information and communication technologies have enabled electronic health and seen a huge expansion over the last decade. This has increased the possibility of self-management of health issues.PurposeTo assess the effectiveness of the Baby Buddy app on maternal self-efficacy and mental well-being three months post-birth in a sample of mothers recruited antenatally. In addition, to explore when, why and how mothers use the app and consider any benefits the app may offer them in relation to their parenting, health, relationships or communication with their child, friends, family members or health professionals. METHODS: We will use a mixed-methods approach, a cohort study, a qualitative element and analysis of in-app data. Participants will be first-time pregnant women, aged 16 years and over, between 12 and 16 weeks of gestation and recruited from five English study sites.Evaluation planWe will compare maternal self-efficacy and mental health at three months post-delivery in mothers who have downloaded the Baby Buddy app compared with those that have not downloaded the app, controlling for confounding factors. Women will be recruited antenatally between 12 and 16 weeks of gestation. Further follow-ups will take place at 35 weeks of gestation and three months post-birth. Data from the cohort study will be supplemented by in-app data that will include, for example, patterns of usage. Qualitative data will assess the impact of the app on the lives of pregnant women and health professionals using both focus groups and interviews.EthicsApproval from the West Midlands-South Birmingham Research Ethics Committee (NRES) (16/WM/0029) and the University of the West of England, Bristol, Research Ethics Committee (HAS.16.08.001).DisseminationFindings of the study will be published in peer reviewed and professional journals, presented locally, nationally and at international conferences. Participants will receive a summary of the findings and the results will be published on Best Beginnings' website.
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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 | 0.072 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".