Optimizing health protocol compliance through supply chain management in Surabaya's COVID-19 response
Bibliographic record
Abstract
This study examines the impact of social and economic factors on community adherence to COVID-19 health protocols in Surabaya, Indonesia, through a supply chain management perspective. It applies the five-component supply chain model encompassing supply chain policies, governance structures, consumer attitudes, process efficiency, and the integration of culture/technology. The primary data is derived from a survey of 119 participants, supplemented by secondary data on national health protocols and local COVID-19 cases. The analysis reveals critical gaps in compliance with health protocols, particularly regarding mask usage, social distancing, and avoiding crowded spaces. Specifically, only 17.6% of religious adherents follow these protocols, while 82.3% do not. In traditional markets, compliance stands at 19.2%, while 80.8% of participants ignore the guidelines. Among the youth, only 12.4% adhere to the protocols, with 87.6% disregarding them. The study highlights the need to improve the supply chain of public health interventions, from awareness campaigns (demand generation) to efficient delivery systems (process optimization) and monitoring mechanisms (evaluation and feedback loops). Emphasizing a supply chain approach, the findings suggest that strengthening the upstream (policy and governance), midstream (public behavior and attitudes), and downstream (cultural and technological adaptations) components can enhance compliance rates and reduce COVID-19 transmission. The study concludes with actionable recommendations, such as increasing public health awareness, strengthening governance frameworks, targeting interventions for vulnerable groups, and fostering multi-stakeholder partnerships to create a resilient health compliance supply chain in Surabaya, 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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".