Study on Implementation of Health Protocols for COVID-19 Prevention in Construction Project in Indonesia
Bibliographic record
Abstract
The construction services industry is one of the industries affected by the COVID-19 outbreak.Most construction project schedules in Indonesia have been postponed or even canceled due to this outbreak.Through the Minister of Public Works and Public Housing Instruction No. 02/IN/M/2020 on the Protocol for Preventing the Spread of COVID-19 in construction projects, the Indonesian government regulates the handling of COVID-19 prevention in construction projects.Unfortunately, the implementation of the Regulation has not been optimal, so it is still necessary to investigate which elements of COVID-19 prevention have been optimally implemented.The goal of this study is to identify the ability to implement COVID-19 prevention protocols in the implementation of construction projects in Indonesia.The research method used in this study was to use surveys and interviews then the data was analyzed using the SPSS statistical program with a quantitative analysis approach.The research findings for COVID-19 preventive health protocols show that COVID-19 prevention strategies have been well implemented in construction projects judging from the average scores, namely isolation if there is an indication of COVID-19 suspect workers (3.867), disinfectants (3.733), small groups (3.733), online meetings (3.667), COVID-19 posters (3.600), health facilities (3.533), and champaign & promotion (3,533).Specifically, this study will provide an overview of the extent of implementation of COVID-19 preventive health protocols in construction projects and input for future policy improvements.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".