An Overview of Continuity of Care Model for Children With Juvenile Diabetes in West Java Region, Indonesia
Bibliographic record
Abstract
INTRODUCTION: The absence of insulin in people with Juvenile Diabetes (JD) requires that they get daily injections of insulin in addition to daily lifestyle that must be managed.Children with JD requires routine care and check-ups from doctors and nurses in hospitals so that the quality of life of children can be optimal. This researchaimed to obtain an overview of JD continuityof care model. METHOD: The design implementedwas descriptive phenomenology. The number of participantswas 18 people, consisting of6 JD patients:3 parents, 2 guidance and counseling teachers, 2 doctors, 1 midwife and 3 nurses. The data were analyzed using Collaizi method. RESULTS: This research identified 8 themesi.e1)health education from doctors and nurses is needed regularly;2) the treating doctor does not change frequently because it will be confusing; 3)looking for treatment because of responsibility and feeling sad for children;4) insulin medicine is obtained according to what the doctor programmed; 5) simplified registration system; 6) fast service;7) the teacher knows how to supervise children in school and 8) the parents need a community as means of sharing information. CONCLUSIONS: Continuity of care model for JD is related to 3 aspectsof service, i.e.continuity of information; communicationcontinuity and continuity of management to becontained in the INKOLA Model (Informasi, Komunikasi and Tata Kelola).The result of research are expected to give information about the need of care so that a proper continuity of care model can be develop.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".