Public health nurse delivered group cognitive behavioral therapy (CBT) for postpartum depression: A pilot study
Bibliographic record
Abstract
OBJECTIVES: Although postpartum depression (PPD) affects 1 in 5 women, just 15% receive treatment. Cognitive Behavioural Therapy (CBT) is a first-line treatment for PPD. The objective of this pilot study was to determine the feasibility and acceptability of public health nurse (PHN)-delivered group CBT for PPD and to determine preliminary estimates of effect. DESIGN: A pre-posttest design was used. Participants provided data before and after the CBT groups. SAMPLE: Seven women who were over the age of 18 and had given birth in the past year participated. MEASUREMENTS: Feasibility and acceptability focused on PHN training, recruitment, retention, and adherence to the intervention. Participants provided data on depression, worry, health care utilization and mother-infant relations. Women and their partners reported on infant temperament. INTERVENTION: Participants attended a 9-week CBT group delivered by two PHNs. RESULTS: The PHN training, CBT intervention and our study protocol were found to be feasible and acceptable to participants. Reductions were seen in depression and worry. The number of health care visits decreased; mother-infant relations improved. CONCLUSIONS: These findings highlight the feasibility of PHN-delivered group CBT for PPD and suggest that it could reduce the burden of PPD on women and their children.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".