Innovative SiKaRen Smartphone Application Model: A Breakthrough in Enhancing IMP Cadres’ Knowledge and Attitudes Toward Family Planning
Bibliographic record
Abstract
Objectives: There are several challenges in the implementation of family planning programs in urban areas, one of which is to enhance the capacity of the urban community institutions (IMPs) as the first level of family counselling and support. The other major constraint is the limited availability of new and efficient training techniques, because conventional training is not very efficient in enhancing the knowledge and beliefs of IMP cadres. In the digital era, the use of smartphone-based technology could be the solution to improve the effectiveness of cadre training in a more flexible and interactive way. This study was conducted to assess the efficacy of the smartphone based SiKaRen application in enhancing the knowledge and attitudes of IMP cadres. Methods: This study used a mixed-methods design, combining quantitative and qualitative approaches. The quantitative aspect employed a quasi-experimental design with pre- and post-tests on two groups: an intervention group using the SiKaRen Applications and a control group with conventional training. The qualitative approach explored the roles, knowledge, and attitudes of 10 Informants through in-depth interviews, focus group discussions (FGDs), and standardized questionnaires. Data were analyzed using statistical tests. Results: The problem of the cadres was that they did not have the required knowledge about the advantages, disadvantages and side effects of various contraceptive methods, hence lacked the confidence of providing advice. When they encounter challenges, they just quit, but they do attempt to help and look for assistance. Furthermore, the role of the cadres is not optimal due to limited facilities, not clearly defined functions, missing documents and lack of innovation. The SiKaRen model based on a smartphone was found to have a significant effect in enhancing the knowledge and attitude of the cadres in the field (p-value < 0.05) and therefore could be a way of solving the problems faced by cadres in the field. Conclusions: The integration of technology into the SiKaRen model enhances the ability of cadres to receive the latest information and to track and monitor family planning participants more effectively. This digital application also enables more precise interventions based on accurate data, meaning that cadres are not only facilitators, but also drivers of family planning awareness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".