Efektivitas Self Efficacy terhadap Pemahaman Tanda Bahaya Kehamilan menggunakan Video dan Buku Kesehatan Ibu dan Anak
Bibliographic record
Abstract
One effort to increase the understanding of pregnant women about the danger signs of pregnancy is to carry out social persuasion in the form of education to improve self-efficacy of understanding the danger signs. Education can be done using a variety of media, namely visual media, audio and audio visual. This literature study aims to find out Effectiveness of Self-Efficacy towards Understanding of Pregnancy Danger Signs using Videos and Books of MCH.The design used in this study is the study of literature. The type of data used is secondary data obtained from journal database searches taken through the internet, both national and international journals. The search results obtained as many as 12 journals that are considered in accordance with the purpose of the study. The results of the literature study show that there is an effect of Self-Efficacy on Understanding of the Signs of danger of pregnancy using Video, there is an effect of Self-Efficacy of Understanding of Signs of danger of pregnancy using the MCH Handbook, and Video media is more effective than the Book of MCH. The conclusion of this literature study is The effectiveness of education delivered is affected by a variety of things, one of which is the media used. The better the media used, the easier the reception of material by respondents. Recommendations from this literature study virtue need to improve the health promotion strategy by advocacy strategy, social support strategy and community empowerment strategy
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".