A 2-Day Postpartum CBT-Based Training for Nurses
Bibliographic record
Abstract
BACKGROUND: Public health nurses (PHNs) are often a first point of contact for postpartum individuals seeking mental health support, but report limited training related to mental health. PURPOSE: To determine whether a two-day cognitive behavioral therapy (CBT)-based training program focused on postpartum maternal mental health can improve PHN perceptions of their ability to deliver CBT techniques, their confidence working with distressed clients, and with managing client resistance to treatment recommendations. METHODS: A convenience sample of 45 PHNs working in the Family Health Division of Niagara Region Public Health in Ontario, Canada were assessed before and after they received a two-day CBT-based training program. Before attending training, PHNs reported their current professional position, years of experience working in public health, and any previous mental health training. Their confidence in delivering CBT techniques, working with distressed clients, and with managing client resistance to treatment recommendations was assessed pre- and post-training. Participants also rated their satisfaction with the training. RESULTS: Statistically significant improvements were seen in confidence using CBT techniques, and in supporting and managing distressed or resistant clients. The two-day training was highly rated overall by participants. Medium to large effect sizes were found for changes in confidence-related questions. CONCLUSIONS: Providing PHNs with brief CBT-based mental health-related training can increase their confidence in this aspect of their practice, and could potentially improve the quality of care they provide.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".