Implementation of a mental health promotion intervention in a high-risk pregnancy unit: the “Ombrelles” program
Bibliographic record
Abstract
BACKGROUND: High-risk pregnancies often result in prolonged hospitalizations, leading to significant emotional and psychological distress for pregnant women and their families. This study aimed to evaluate the implementation of the "Ombrelles" mental health promotion program in a high-risk pregnancy unit in a Canadian tertiary hospital. METHODS: A comprehensive needs assessment was conducted using both quantitative and qualitative methods, including a literature review, surveys, semi-structured interviews, and expert committee meetings. The findings were used to develop the "Ombrelles" program, which has three main components: an educational component with educational in-person or virtual workshops, online courses and videos, a structured support component with activities promoting socialization and relaxation and the promotion of a supportive environment. Surveys were then conducted among inpatients and hospital staff to measure the relevance and satisfaction regarding this intervention. RESULTS: During the first two years of rolling out the program, 123 women admitted in the high-risk pregnancy unit and 30 hospital staff were recruited. Results showed proficient resource utilization, high satisfaction (92.2%), and staff recognition of program relevance (55.2%). The "Ombrelles" program effectively met the identified needs, emphasizing a partnership approach between healthcare teams and patients. Areas for improvement were identified, including addressing technical difficulties and avoiding repetition of themes. CONCLUSION: The "Ombrelles" program effectively addressed the identified needs of pregnant women hospitalized for high-risk pregnancies. It provided essential support, educational resources, and opportunities for socialization. The program's development process emphasized partnership between healthcare teams and patients, ensuring its appropriateness and effectiveness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".