Early Educational and Behavioral RCT Interventions to Reduce Maternal Symptoms of Psychological Trauma Following Preterm Birth
Bibliographic record
Abstract
Early behavioral and educational interventions have been developed to reduce maternal symptoms of psychological trauma (depression, anxiety, parenting stress, acute stress disorder, and posttraumatic stress disorder) following preterm birth (PTB). Aims of this systematic review were to critically assess study methodology and provide a synthesis of existing randomized control trial (RCT) interventions and to estimate effects of the interventions in reducing the maternal symptoms across studies. Four electronic databases were systematically searched to locate relevant RCTs using preestablished eligibility criteria. Data from 8 qualifying RCTs were synthesized. Two reviewers independently assessed study methodology using appraisal checklists. Considerable heterogeneity precluded calculation of pooled estimates. There is evidence that mothers of very preterm infants of low-birth-weight experience major depression for up to 12 months following PTB. Sound interventions implemented during and following infant hospitalization and grounded in coping and self-regulation had a small to moderate effect in reducing maternal depression and anxiety and parenting stress for up to 2 months and 12 months respectively. Clinicians can use existing evidence to help guide best practices. Future high-quality RCTs and meta-analysis require that researchers improve study methodology and include analysis of data on maternal hormonal stress and history of psychological symptoms including during the pregnancy.
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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".