Public health nurses’ experiences learning and delivering a group cognitive behavioral therapy intervention for postpartum depression
Bibliographic record
Abstract
OBJECTIVES: Public Health Nurses (PHNs) often provide support to women with postpartum depression (PPD) in the absence of specialized training. The objective of this study was to explore the experiences of six PHNs who were trained to deliver a group cognitive behavioral therapy (CBT) intervention for PPD in the public health setting, and to describe how learning and delivering this intervention affected their professional roles and personal lives. DESIGN: This qualitative study employed a phenomenological approach. SAMPLE: Six PHNs who completed the CBT training program and delivered at least one CBT group in their community. MEASUREMENTS: Individual in-depth interviews were conducted and transcribed verbatim. Transcripts were analyzed according to thematic derivation procedures. RESULTS: The themes that emerged from the interviews with the PHNs included: (a) components of the CBT training program that nurses most valued, (b) benefits of training for their professional role as a PHN, (c) implications for practice, and (d) using CBT skills in their personal lives. CONCLUSIONS: The provision of CBT training to PHNs may not only positively impact their work with clients with mental illness, but may also have the potential to provide broader clinical and professional benefits for these skilled professionals and their other clients.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".