The Effect of Education Program of the Couples Based on the BASNEF Model on Spousal Support and Mental Health of Pregnant Women
Bibliographic record
Abstract
Pregnancy is a natural physiological phenomenon while during pregnancy women are exposed to physical and mental changes which can affect their health and fetus. This study aimed to study the effect of young couples programs based on the BASNEF model on the mental health spouse support of pregnant women in 2014 years. This study was a quasi-experimental study. Statistical population was selected including 125 couples who referred to health centers of Hormozgan University of Medical Sciences. Centers were also selected through random cluster among health centers of Hormozgan and 2 centers were randomly assigned for implementation of intervention and 2 centers were selected as control. Training intervention was implemented on the basis of the BASNEF model which includes young couples training as face to face for four sessions of 60 minutes at health centers. 4 weeks after completing the training, post-test was performed. Instruments used in this study include 3 questionnaires: demographic information, BASNEF Model, spousal support, mental health assessment questionnaire, which was completed before and 4 weeks after the intervention in both intervention and control groups. Data was extracted and was analyzed using SPSS21 software. Average of knowledge, attitude, enabling factors, subjective norms, behavior in the intervention group showed significant differences than the control group (P <0.001). The spouses support and Mental health scores in the intervention group showed no significant difference than the control group (P <0.001). The programmed trainings using health education model be used instead of education. Educating young couples be used instead of individual care of pregnant women and also help to promote family health with the presence of their wives in prenatal care and applied proper education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".