Electronic Brief Intervention and Text Messaging for Marijuana Use During Pregnancy: Initial Acceptability of Patients and Providers
Bibliographic record
Abstract
BACKGROUND: Marijuana is the most widely used illicit substance during pregnancy. Technology-delivered brief interventions and text messaging have shown promise in general and pregnant samples but have not yet been applied to marijuana use in pregnancy. OBJECTIVE: The objective of the study was to evaluate, among pregnant women and prenatal care providers, the acceptability of an electronic brief intervention and text messaging plan for marijuana use in pregnancy. METHODS: Participants included patients (n=10) and medical staff (n=12) from an urban prenatal clinic. Patient-participants were recruited directly during a prenatal care visit. Those who were eligible reviewed the interventions individually and provided quantitative and qualitative feedback regarding software acceptability and helpfulness during a one-on-one interview with research staff. Provider-participants took part in focus groups in which the intervention materials were reviewed and discussed. Qualitative and focus group feedback was transcribed, coded manually, and classified by category and theme. RESULTS: Patient-participants provided high ratings for satisfaction, with mean ratings for respectfulness, interest, ease of use, and helpfulness ranging between 4.4 and 4.7 on a 5-point Likert scale. Of the 10 participants, 5 reported that they preferred working with the program versus their doctor, and 9 of 10 said the intervention made them more likely to reduce their marijuana use. Provider-participants received the program favorably, stating the information presented was both relevant and important for their patient population. CONCLUSIONS: The findings support the acceptability of electronic brief intervention and text messaging for marijuana use during pregnancy. This, combined with their ease of use and low barrier to initiation, suggests that further evaluation in a randomized trial is appropriate.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".