Integrated Immunization Information System in Indonesia: Prototype Design Using Quantitative and Qualitative Data
Bibliographic record
Abstract
BACKGROUND: As the volume of immunization records increases, problems with fragmented records arise, especially since the majority of records in developing countries, including Indonesia, remain paper based. Implementing an immunization information system (IIS) offers a solution to this problem. OBJECTIVE: In this study, we designed an integrated IIS prototype in Indonesia using the design science research (DSR) methodology. METHODS: The stages of the DSR methodology followed in this study included identifying problems and motivating and defining objectives for a solution, design and development, demonstration, evaluation, communication, and drawing conclusions and suggestions. Specifically, this study began with problem formulation and a literature review. We then applied quantitative (questionnaire with 305 members of the public) and qualitative (interviews with 15 health workers including nurses, midwives, and doctors) data collection approaches. RESULTS: The resulting high-fidelity prototype follows the 8 golden rules. There are 2 IIS designs, one for the public as immunization recipients and another for health workers. The functionalities include immunization history, schedule, recommendations, verification, certificates, reminders and recalls, coverage, monitoring, news, and reports of adverse events. Evaluation of the prototype was carried out through interviews and a questionnaire designed according to the System Usability Scale (SUS) and Post-Study System Usability Questionnaire (PSSUQ). The SUS value was 72.5 or "Good (Acceptable)," while the system usefulness, information quality, interface quality, and overall value on the PSSUQ were 2.65, 2.94, 2.48, and 2.71, respectively, which indicate it has an effective design. CONCLUSIONS: This provides a guide for health facilities, health regulators, and health application developers on how to implement an integrated IIS in Indonesia.
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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 | 0.034 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".