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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".