Feasibility and Perception of Using Text Messages as an Adjunct Therapy for Low-Income, Minority Mothers With Postpartum Depression
Bibliographic record
Abstract
BACKGROUND: Postpartum depression (PPD) is the most common medical problem among new mothers that can have a negative impact on infant health. Traditional treatments are often difficult for low-income mothers to complete, particularly given the numerous barriers families face. OBJECTIVE: Among low-income, primarily racial, and ethnic minority mothers with postpartum depression, our aim was to evaluate (1) the feasibility of sending supportive text messages, and (2) the perception of receiving private, supportive text messages for postpartum depression. METHODS: Mothers found to be at risk for postpartum depression received supportive text messages four times weekly for 6 months in addition to receiving access to traditional counseling services based within an academic pediatric office. Feasibility was evaluated along with cellular and text messaging use, access, and perception of the message protocol. Perception of the message protocol was evaluated at study completion via a Likert scale questionnaire and open-ended qualitative survey. RESULTS: In total, 4158/4790 (86.81%) text messages were successfully delivered to 54 mothers over a 6-month period at a low cost (US $777.60). Among the 96 scripted messages, 37 unique messages (38.54%) allowed for a response. Of all sent messages that allowed for responses, 7.30% (118/1616) were responded to, and 66.1% of those responses requested a call back; 46% (25/54) of mothers responded at least once to a text message. Mothers felt that messages were easily received and read (25/28, 89%) and relevant to them personally (23/28, 82%). Most shared texts with others (21/28, 75%). CONCLUSIONS: Text messaging is feasible, well-accepted, and may serve as a simple, inexpensive adjunct therapy well-suited to cross socioeconomic boundaries and provide private support for at-risk mothers suffering from postpartum depression.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".