Factors Influencing the Reporting Time of Online-Based Recording and Reporting Systems in Public Health Center of Semarang City
Bibliographic record
Abstract
ABSTRACT The target of timely reporting of SP3 online in Semarang City in the first quarter of 2017, amounted to 72% of Public Health Centers on time. Quarter II of 2017 was 62%. This is not in accordance with the target set by the Semarang City Health Office, which is 80%. The purpose of the study was to determine the factors that influence the timeliness of monthly SP3 reporting in Semarang City. This is an observational analytic research with case-control design. The sample set was 14 cases and 14 controls. The research instrument used was structured questionnaire. The results showed age factor (p value = 0.018; OR = 10.8), incentive (p value = 0.023; OR = 9.1), workload (p value = 0.008; OR = 13.4), leader support (p value = 0.008; OR = 15), supporting facilities (p value = 0.033; OR = 13) influenced the timeliness of SP3 reporting and years of service factor (p value = 0.7; OR = 1.8), computer skills (p value = 0.55; OR = 2.07), education (p value = 1; OR = 1.4), job training (p value = 0.5; OR = 2.07), and co-worker support (p value = 0.02; OR = 2.5) had no influence on the timeliness of SP3 reporting. ABSTRAK Target ketepatan waktu pelaporan SP3 online Puskesmas Kota Semarang triwulan I tahun 2017, sebesar 72% puskesmas tepat waktu. Triwulan II tahun 2017 sebesar 62%. Hal ini tidak sesuai dengan target yang ditetapkan oleh Dinas Kesehatan Kota Semarang, yaitu 80%. Tujuan penelitian untuk mengetahui faktor yang mempengaruhi ketepatan waktu pelaporan Sistem Pencatatan Puskesmas (SP3) Bulanan Kota Semarang. Jenis penelitian adalah observasional analitik dengan rancangan case control. Sampel yang ditetapkan sebesar 14 kasus dan 14 kontrol. Instrumen penelitian berupa kuesioner terstruktur. Hasil menunjukkan faktor umur (p value=0,018; OR=10,8), insentif (p value=0,023; OR=9,1), beban kerja (p value=0,008 ; OR=13,4), dukungan pimpinan (p value=0,008; OR=15), fasilitas pendukung (p value=0,033; OR=13) mempengaruhi ketepatan waktu pelaporan SP3 dan faktor masa kerja (p value=0,7; OR=1,8), kemampuan teknik komputer (p value=0,55; OR=2,07), pendidikan (p value=1; OR=1,4), pelatihan kerja (p value=0,5; OR=2,07), dan dukungan rekan kerja (p value=0,02; OR=2,5) tidak mempengaruhi ketepatan waktu pelaporan SP3.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".