Statistical Analysis of Logistics Management Impact on Medical Device Indicators in Indonesian Island Clinics
Bibliographic record
Abstract
Background: Public health centers have a strategic role in the implementation of primary health services, to meet minimum service standards. The ASPAK application is used as an instrument for monitoring facilities, infrastructure, and medical devices, but the achievement of medical device indicators in Banggai Laut district is still very low, at 27.87%, which indicates obstacles in the logistics management of medical devices. This study aims to analyze the relationship between logistics management and medical device indicators on the ASPAK Application. Methods: This study employed a quantitative approach with a cross-sectional design. Statistical analysis was conducted using the Pearson Product-Moment Correlation test and multivariate regression to examine the relationship between logistics management and medical device indicators on the ASPAK application across 10 health centers in Banggai Laut District. The research was carried out from September to December 2024, involving a total population of 70 individuals, all of whom were included as the study sample. Result: There is a relationship between logistics management sub-variables and medical device indicators (r=0.583-0.659; p=0.000). Multivariate analysis, planning, and deletion are significantly related (y = 6.877 + 0.437 planning + 0.481 deletion), with an R-square value of 0.638, which means 63.8% of the variation in indicator achievement is explained by the model. Conclusion: Planning and deletion have a positive correlation and are the largest contributors to the fulfillment of the ASPAK medical device indicator. This finding emphasizes the importance of both aspects in supporting indicator achievement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".