Development and assessment of a mobile phone-based intervention to reduce maternal depression and improve child health
Bibliographic record
Abstract
Introduction Postnatal depression is known to cause disability and suffering in women and negative consequences both for their infants and their families, with huge costs globally. Several studies from low and middle income countries (LAMIC) have demonstrated that effectively delivered psychological interventions are cost effective for improving maternal and child health, but access to these interventions is limited in both the low and high income countries. Objective The objective of the study is to develop and test a mobile phone-based intervention (TechMotherCare), which will include components of cognitive behavioural therapy (CBT) and child development related psychoeducation. Aim The aim of the study is to examine the feasibility and acceptability of the TechMotherCare intervention. Methods A total of 36 participants will be recruited from health centers in Karachi, Pakistan for this 2-arm randomized pilot study. The TechMotherCare App intervention will be based on principles of CBT and learning-through-play (LTP) a parenting intervention and will assess the real-time depressive symptoms of participants and respond, using intelligent real time therapy (iRTT) dependent on symptoms reported by participants. Results Outcome assessments will be completed after 3 months (end of intervention). In-depth qualitative interviews will also be conducted with participants pre- and post-intervention. The trial is ongoing and we will present both the qualitative and quantitative results. Conclusions The results of this pilot trial will inform the design of a larger randomised controlled trial using a mobile based technology platform to address the huge treatment gap in LAMICs. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".