Teaching by texting to promote positive health behaviours in pregnancy: a protocol for a randomised controlled trial of SmartMom
Bibliographic record
Abstract
INTRODUCTION: Prenatal education is associated with positive health behaviours, including optimal weight gain, attendance at prenatal care, acceptance of routine screening tests, smoking cessation, decreased alcohol consumption and breast feeding. Adoption of these behaviours has been associated with reduced rates of caesarean birth, preterm birth and low birth weight. Barriers to prenatal class attendance faced by parents in Canada include geography, socioeconomic status, age, education, and, among Indigenous peoples and other equity-deserving groups, stigma. To address the need for easily accessible and reliable information, we created 'SmartMom', Canada's first prenatal education programme delivered by short message service text messaging. SmartMom provides evidence-based information timed to be relevant to each week of pregnancy. The overall goal of SmartMom is to motivate the adoption of positive prenatal health behaviours with the ultimate goal of improving health outcomes among women and their newborns. METHODS AND ANALYSIS: We will conduct a two-arm single-blinded randomised controlled trial. Blinding of participants to trial intervention will not be possible as they will be aware of receiving the intervention, but data analysts will be blinded. Our primary research questions are to determine if women experiencing uncomplicated pregnancies randomly assigned to receive SmartMom messages versus messages addressing general topics related to pregnancy but without direction for behaviour change, have higher rates of: (1) weight gain within ranges recommended for prepregnancy body mass index and (2) adherence to Canadian guidelines regarding attendance at prenatal care appointments. ETHICS AND DISSEMINATION: The study has been granted a Certificate of Approval, number H22-00603, by the University of British Columbia Research Ethics Board. To disseminate our findings, we are undertaking both integrated and end-of-grant knowledge translation, which will proactively involve potential end-users and stakeholders at every phase of our project. TRIAL REGISTRATION NUMBER: NCT05793944.
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 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 | 0.058 | 0.063 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.105 | 0.017 |
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".