Impact of mHealth interventions during the perinatal period on maternal psychosocial outcomes: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: This review aims to evaluate the effectiveness of mother-targeted mobile health (mHealth) education interventions during the perinatal period on maternal psychosocial outcomes in high-income countries. INTRODUCTION: Mobile health (i.e. mHealth) is defined as the use of mobile devices to transmit health content and services. The use of mHealth to provide education and support to mothers is a growing field of health innovation. Mothers seek health information online during the postpartum period to learn about health concerns and get advice and support. Despite the potential benefits of mHealth, the potential impact on maternal psychosocial outcomes requires further evaluation. INCLUSION CRITERIA: The review will consider studies that include mHealth interventions targeting mothers in high-income countries. The mHealth education interventions must occur during the antenatal or postnatal period. This review will consider studies that compare the intervention to any comparators. Studies published in English from 2000 will be included. METHODS: The search strategy will aim to locate both published and unpublished studies. Following the search, all identified citations will be collated and duplicates removed. Titles and abstracts will be screened and full text of selected citations will then be assessed in detail against inclusion criteria. The results of the search will be reported in full in the final systematic review. Eligible studies will be critically appraised by two independent reviewers. Data extracted will include specific details about the interventions, populations, study methods and outcomes. Studies will be pooled in statistical meta-analysis or presented in narrative form including tables and figures.
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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 | 0.065 | 0.061 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.017 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".