The correlation between knowledge and intention with self-efficacy of pregnant women to attend antenatal care at healthcare
Bibliographic record
Abstract
In Indonesia, the utilization of antenatal care at healthcare professionals was only 66%, and this figure dropped during a delivery. As much as 46% of pregnant women who attend antenatal care at healthcare professionals did not carry birth in healthcare facility. This study aims to explain the correlation between knowledge and intention with self-efficacy of pregnant women to get antenatal care. This was a quasi-experimental research with pre and post one group study. The samples included pregnant women in Balikpapan city, who had entered the second trimester of pregnancy. Sampling was carried out using simple random sampling technique by using a random number generator program is research randomizer to determine the group. Total sample was 20 pregnant women. The results showed that: 1) There was a significant correlation between knowledge and self-efficacy (p = .001); 2) There was a significant correlation between intentions and self-efficacy (p = .017). This study concluded that self-efficacy of pregnant women was high, the majority of pregnant women were not in the age of risk, pregnant women with high and average level of parity had a high knowledge. There were pregnant women who had high knowledge but did not get antenatal care from healthcare professionals. Most pregnant women had intention to attend antenatal care at healthcare professionals, but there were still pregnant women who attended antenatal care less than the prescribed standards that is at least four times during pregnancy.
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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 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".