Feasibility of Delivering a Cognitive Behavioural Therapy-Based Resilience Curriculum to Young Mothers by Public Health Nurses
Bibliographic record
Abstract
Background: Young mothers have higher rates of mental health problems, yet can be difficult to engage in care. Few interventions exist targeting the full range of mental health problems these women face. While transdiagnostic psychotherapies have been utilized in adolescent groups, they have not been tested in young mothers. Objective: Our objective was to examine the feasibility and acceptability of a public health nurse-delivered transdiagnostic CBT-based resilience curriculum for young mothers in a supported school setting, and to determine preliminary estimates of the program’s effects. Methods: 56 mothers, 21 years of age or younger were recruited from a supported high school program in Canada. Using a pretest/post-test design with no control group, measures of maternal depression, anxiety, emotion regulation, and offspring behaviour were collected immediately before and after the completion of the weekly 10-session intervention. Results: The intervention was feasible and acceptable to young mothers. While few statistically significant changes were noted in the complete sample, for those with moderate-severe depression at baseline, program participation resulted in clinically meaningful improvements in depression, anxiety, and emotion regulation. Conclusion: Provision of a transdiagnostic CBT-based resilience-building program delivered by public health nurses in a supported school setting was both feasible and well-tolerated. Given the preliminary nature of this study, its clinical utility is unclear, though it may have benefits for young mothers with more significant mental health problems at baseline.
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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 | 0.008 |
| 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) | 0.003 | 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".