Peer support for postpartum depression: volunteers' perceptions, recruitment strategies and training from a randomized controlled trial
Bibliographic record
Abstract
A randomized controlled trial evaluated the effect of telephone-based peer support (mother-to-mother) on preventing postpartum depression among high-risk mothers. This paper reports volunteers' perceptions, which showed that peer support is an effective preventative intervention. Two-hundred and five (205) volunteers were recruited and trained to provide peer support to 349 mothers randomized to the intervention group. Volunteers' perceptions were measured at 12 weeks using the Peer Volunteer Experience Questionnaire, completed by 69% (121) of the 175 volunteers who provided support to at least one mother. Large majorities felt that the training session had prepared them for their role (94.2%), that volunteering did not interfere with their lives (81.8%) and that providing support helped them grow as individuals (87.8%). Over 90% stated that they would become a peer volunteer again, given the opportunity. Recruitment and retention of effective volunteers is essential to the success of any peer-support intervention. Results from this study can assist clinicians and program planners to provide effective training, sufficient on-going support and evaluation and appropriate matching of volunteers to mothers who desire peer support and are at high risk of 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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".