Use of Text Messaging for Postpartum Depression Screening and Information Provision
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the feasibility of using text messages to enhance mental health screening and education of women in the immediate postpartum period. METHODS: A total of 937 postpartum women were recruited from an obstetrics and gynecology clinic of a large urban hospital. Participants received a text message containing a two-question screen for postpartum depression every two weeks and three text messages per week about postpartum mental health for the first 12 weeks postpartum. Those who screened positive were administered the Edinburgh Postnatal Depression Scale. They were matched with a subset of women who were also assessed with the Edinburgh Postnatal Depression Scale after screening negative for depression with the text messaging screen. At 12 to 13 weeks postpartum, all participants received an online survey assessing satisfaction with the text messages. RESULTS: Of 937 participants, 126 (13%) screened positive. Agreement between the texted screen and the Edinburgh Postnatal Depression Scale was moderate (κ=0.45), with good sensitivity (0.90, 95% confidence interval [95% CI]=0.81-0.96) and specificity (0.82, 95% CI=0.79-0.85). Nine hundred thirty (99%) participants responded to at least one of the six texted screens, whereas 632 (67%) responded to all six. Of the 589 (63%) who responded to the satisfaction survey, 459 (78%) recommended that all women be screened for postpartum depression via text messaging and that all women in the postpartum period be sent information texts about postpartum depression (N=504, 91%). CONCLUSIONS: Using text messaging technology to screen women for postpartum depression and provide information on postpartum mental health appears to be sensitive, feasible, and well accepted.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".