Effectiveness of the ‘Home‐but not Alone’ mobile health application educational programme on parental outcomes: a randomized controlled trial, study protocol
Bibliographic record
Abstract
AIMS: The aim of this study was to describe a study protocol that evaluates the effectiveness of the 'Home-but not Alone' educational programme delivered via a mobile health application in improving parenting outcomes. BACKGROUND: The development in mobile-based technology gives us the opportunity to develop an accessible educational programme that can be potentially beneficial to new parents. However, there is a scarcity of theory-based educational programmes that have incorporated technology such as a mobile health application in the early postpartum period. DESIGN: A randomized controlled trial with a two-group pre-test and post-test design. METHODS: The data will be collected from 118 couples. Eligible parents will be randomly allocated to either a control group (receiving routine care) or an intervention group (routine care plus access to the 'Home-but not Alone' mobile health application. Outcome measures comprise of parenting self-efficacy, social support, parenting satisfaction and postnatal depression. Data will be collected at the baseline (on the day of discharge) and at four weeks postpartum. DISCUSSION: This will be an empirical study that evaluates a theory-based educational programme delivered via an innovative mobile health application on parental outcomes. Results from this study will enhance parenting self-efficacy, social support and parenting satisfaction, which may then reduce parental risks of postnatal depression.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".