Handling Contraceptive Acceptors’ Complaints by Using Android-Based Application
Bibliographic record
Abstract
INTRODUCTION: Contraception is a method which used to prevent pregnancy and to reduce the maternal mortality, particularly in 4T condition; too young to give birth, too often to give birth, too close in spacing between births, and too old to give birth. However, the number of Drop Out from contraceptive in Indonesia is still high due to the concern of contraceptive acceptors about the side effects of contraception and the inaccuracy of re-injection schedule causing the acceptors to stop using contraception. Conventional method has been used by publics to manage the contraception for a long time. In this case, conventional method has been used to deal with the side effects of contraceptive use. By using the conventional method, the acceptor must visit the public health to consult their complaints to a midwife. Moreover, midwife will give the family planning control card to the acceptor which involves re-injection’s schedule. However, many acceptors do not come to do re-injection as the schedule because they do not have an automatic reminder to remind them. Thus, the researcher interested to create a contraceptive’s application in order to help the acceptors managing their schedule. MATERIAL & METHODS: This study employed comparative method to find out the comparison on the attitude of contraceptive acceptors in solving the problems by using the Smart Contraception application and conventional method. By using purposive sampling technique, 44 respondents were chosen in this study. The respondents were divided into two groups involving 22 respondents who used Smart Contraception application and 22 respondents who used conventional method. Then, the data were analyzed by using Chi-square test. RESULTS: The result of the study showed that the use of Smart Contraception application was better than conventional method in handling the problems experienced by contraceptive acceptors. The statistical result on the attitude of contraceptive acceptors in handling the problems of contraception shows p-value 0.026 (<0.05) and p-value 0.023 (<0.05) on the accuracy of re-injection schedule, which means that there was difference between the use of Smart Contraception application and conventional method. CONCLUSION: There was difference between the use of Smart Contraception application and conventional method. Furthermore, the use of Smart Contraception was better than conventional method in handling the problems experienced by contraceptive acceptors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".