Hubungan Motivasi Terhadap Kinerja Kader Pada Program Gerakan Menekan Angka Kematian Ibu Dan Bayi (Gemakiba) Di Kelurahan Sukorame Kota Kediri Wilayah Kerja Puskesmas Sukorame
Bibliographic record
Abstract
The indicator that can be used to measure of status health mother in certain region such as using number of maternal mortality (Angka Kematian Ibu / AKI). AKI is one of indicator that sensitive about quality and an access to facility health service. One of factor that contributed to maternal mortality is factors that complicate of process handling emergency such as TIGA TERLAMBAT. It used to press AKI, the government made programs. The program is Program Perencanaan Persalinan dan Pencegahan Komplikasi (P4K) and Kediri was held program Gerakan Menekan Angka Kematian Ibu dan Bayi (GEMAKIBA). The aim of this research is to know the relationship of motivation performance cadre at GEMAKIBA’s program at Sukorame district Kediri city region performance of clinic Sukorame. The research design is survey analytic. The approach is cross sectional, the population is 45 persons of cadre. The sampling uses simple random sampling with 41 respondences of cadre that fulfil of inclusive criteria. The instrument used questioner to assess of motivation, checklist to assess of performance and recapitulation sheet to make a note the result of data. Then the data was analyzed with correlation spearman rank that showed there is a relathionship between motivation and performance cadre at GEMAKIBA’s program. Based on the result of the research above it should be became a material to maintain and enhance motivation for more comprehensive program’s outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".