Strengthening the Role of Cadres with Digital Literacy in iPosyandu Application-Based Recording and Reporting
Bibliographic record
Abstract
Posyandu is part of the Community-Sourced Health Efforts (UKBM), a health information system in maternal and child health services. Currently, health recording and reporting are still in paper form. Therefore, an iPosyandu application facilitates and accelerates cadres in recording and reporting. This activity was carried out in March 2023 to 145 cadres in Pagedangan District, Tangerang Regency, Banten. The method used was training on recording and reporting based on the iPosyandu application, where cadres were given a questionnaire on the use of the iPosyandu application. The results of this activity showed that most of the cadres had the characteristics of age 31-40 years (41.4%), the last education was high school (55.2%), the occupation was taking care of the household (97.9%), and the experience of being a cadre for 1-5 years (51%), and most of the cadres serving in the posyandu had middle strata (41%). Most cadres agreed to use the iPosyandu application because it can make reports quickly (83.7%), and cadres felt that the iPosyandu application had all the functions based on cadres' abilities (87.1%). In addition, there is a relationship between the last education of cadres and the use of iPosyandu applications (p-value < 0.005; r value > 1). This community service activity concludes that cadres have good digital literacy in using the iPosyandu application, so the system for recording and reporting the results of posyandu activities can run well. Keywords: iPosyandu, cadres, digital literacy, recording, reporting.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".