Evaluation of Technology-Based Peer Support Intervention Program for Preventing Postnatal Depression: Protocol for a Randomized Controlled Trial.
Bibliographic record
Abstract
BACKGROUND: Multiple international agencies, including the World Health Organization and the International Monetary Fund, have emphasized the importance of maternal mental health for optimal child health and development. Adequate social support is vital for the most vulnerable to postpartum mood disorders. Hence, an urgent need for sustainable social support programs to aid mothers ease into their new parenting role exists. OBJECTIVE: This study protocol aims to examine the effectiveness of a technology-based peer support intervention program among mothers at risk for postnatal depression in the early postpartum period. METHODS: A randomized controlled 2-group pretest and repeated posttest experimental design will be used. The study will recruit 118 mothers from the postnatal wards of a tertiary public hospital in Singapore. Eligible mothers will be randomly allocated to receive either the peer support intervention program or routine perinatal care from the hospital. Peer volunteers will be mothers who have experienced self-reported depression and will be receiving face-to-face training to support new mothers at risk of depression. Outcome measures include postnatal depression, anxiety, loneliness, and social support. Data will be collected at immediate postnatal period (day of discharge from the hospital), at fourth week and twelfth week post childbirth. RESULTS: The recruitment and training of peer support volunteers (N=20) ended in June 2017, whereas recruitment of study participants commenced in July 2017 and is still ongoing. The current recruitment for new mothers stands at 73, with 36 in the control group and 37 in the intervention group. Data collection is projected to be completed by May 2018. CONCLUSIONS: This study will identify a potentially effective and clinically useful method to prevent postnatal depression in new mothers, which is the top cause of maternal morbidity. Receiving social support from others who share similar experiences may enhance the positive parenting experiences of mothers, which in turn can improve the psychosocial well-being of the mothers, tighten mother-child bond, and enhance overall family dynamics for mothers and infants. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number ISRCTN14864807; http://www.isrctn.com/ISRCTN14864807 (Archived by WebCite at http://www.webcitation.org/6xtBNvBTX).
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.066 | 0.009 |
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".