Intervention “Premenopause Empowerment Model (PEM)” to Change Health Belief in the Control of Perimenopause Complaints in Pematangsiantar City, Indonesia
Bibliographic record
Abstract
Prevention efforts against the effects of decreased estrogen hormones are very noteworthy, these conditions can lead to a variety of health problems and are very much complained of in the perimenopause period. This research is to achive the improvement of premenopausal women’s Health Belief by using PEM for the perimenopause period. The research will involve two Group Design pretest–Posttest with control group that took the location in two sub districts involving women aged 40 yr to 45 yr, as many as 70 people The quasi-experimental designed study employed two groups: the pretest–posttest and control group and was conducted at two sbdistricts: Kahean, for the control group, and Tomuan, for the intervention group. Data processed with univariate and bivariate analysis, sufficient with t test. The results showed that there was an increase in the health belief score before the intervention (59.74 ± 7.01), after the intervention (76.89 ± 9.70) with P = < 0.001. The provision of information based on Health Belief Model in the study able to change the perception of premenopause community in the village Tomuan, shown by increasing the value of the perception of seriousness and vulnerability to the interference that will be experienced and can considering the gains to be gained so decided to behave as expected. It is recommended that women empowerment premenopause with sustainable PEM and the development of health service efforts in the community through Peer 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".