Responsiveness in Family Planning Service Using Android-Based Smart Contraception Application
Bibliographic record
Abstract
Introduction: Midwifery documentation is a process of recording, reporting, and storing the information or meaningful data which based on the accurate and complete written communication. The documentation is an evidence of the implementation of midwifery care which is useful for midwives, patients, and other health workers. Materials and Methods: The study employed comparative method with quasi-experiment design. By employing purposive sampling technique, 84 acceptors of family planning were chosen. Moreover, they were divided into two groups; 42 acceptors who used conventional documentation and 42 acceptors who used Smart Contraception documentation. The data were analyzed by using Univariate analysis to find out the mean and Bivariate analysis by using Chi-square test. Results: From 29 out of 42 (69%) samples, it was found that the accessibility of documentation by using Smart Contraception was easier than using conventional method, meanwhile 13 out of 42 (31%) samples found it difficult. In conventional method, 23 out of 42 (54,8%) samples found it easier in documenting by using conventional method than using Smart Contraception application, meanwhile 19 out of 42 samples found it difficult. The statistical result showed that p value 0.006 (<0.05) which means that there was difference between the use of Smart Contraception application and conventional method in doing documentation. Conclusion: Based on the results of the study, it can be concluded that Smart Contraception was better on documentation accessibility than conventional documentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".