A randomized‐controlled trial to examine the effectiveness of the ‘Home‐but not Alone’ mobile‐health application educational programme on parental outcomes
Bibliographic record
Abstract
AIM: To examine the effectiveness of Home-but not Alone, a postnatal psychoeducational programme delivered via a mobile-health application for parents during the early postpartum period to improve parenting outcomes. BACKGROUND: The early postpartum period is often a challenging but crucial period for new parents. Supportive educational programmes delivered via mobile-health applications are needed to improve parenting outcomes. DESIGN: A randomized-controlled two-group pre-test and post-test design was adopted. METHODS: Data were collected over 6 months (December 2015-May 2016) from 250 participants in a tertiary teaching hospital. They were randomly assigned to the intervention (n = 126) or control (n = 124) groups. Parental self-efficacy, social support, postnatal depression and parenting satisfaction were measured using reliable and valid instruments. A linear mixed method analysis was used to compare the percentage change of all outcome variables. RESULTS: The intervention group had statistically significant improvements for parental self-efficacy, social support and parenting satisfaction at 4 weeks postpartum compared with the control group. Postnatal depression scores did not show any significant improvement compared with the control group. CONCLUSION: The mobile-health application was effective in improving parental self-efficacy, social support and parenting satisfaction. Hence, it should be introduced and carried out in routine care by nurses. Further studies should focus on evaluating the effects of this programme in reducing postnatal depression amongst parents. TRIAL REGISTRATION NUMBER: ISRCTN99092313.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 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".