Penerapan Protokol Kesehatan Hotel sebagai Langkah Preventif pada Masa Pandemi
Bibliographic record
Abstract
Hotel is one of the places to stay for tourists while on their tour. During the Covid-19 pandemic, hotel operations still running but with limited activities. Due to the relaxation period, the number of usable rooms increase along with the implementation of the Health Protocol as a preventive way in every hotel facility. This study aims to determine the application of hotel Health Protocols in Bandung. The method used is descriptive quantitative in assessing the importance of Health Protocols in each hotel services in providing safety and comfort for tourists during their stay. The results showed that the flow of hotel Health Protocol application was socialized and implemented for both employees and visitors. The number of hotels visited during the second quarter of 2020 along relaxation is 225 facilities or 56% of all hotels in Bandung City. Adherence to the implementation of the Health Protocol in the first 2 weeks was 76% and increased to 100% at the second 2 weeks or there was an increase in adherence by 25%. The total increase in compliance with hotel Health Protocol implementation is 25%. The conclusion of this study is that the implementation of the hotel Health Protocol has increased, which is one of the guarantees of tourist safety and preventive steps to break the chain of Covid-19 transmission in hotels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".