Analisis Trend Dan Grafik Barber Johnson Pada Efisiensi Tempat Tidur Di Rumah Sakit X Kota Bandung
Bibliographic record
Abstract
Trend analysis is a statistical analysis method used for planning and evaluating efforts to minimize risk for the better. The purpose of this study was to analyze trends and barber johnson charts on the efficiency of bed use at X Hospital, Bandung City. This type of research is a qualitative method with a descriptive approach. Observations and interviews were carried out with data processing officers and medical record reporting officers, while secondary data was obtained from RL3 Year 2020 at Hospital X Bandung City. Data analysis using least square trend method and Barber Johnson chart. The results showed that the trend of BOR and BTO in Quarter I-IV of 2020 decreased. The trend of AvLOS and TOI in Quarter I and II increased, while in Quarter III and IV it decreased. Based on the results of the study, it can be analyzed that the use of beds at Hospital X Bandung City in 2020 has not been efficient, only reaching 20-60% while the standard value according to Barber Johnson is 75-85%, but it can be predicted that the TOI indicator will be more efficient, while the BOR indicator , AvLOS, and BTO are increasingly inefficient because their values are getting further away from the predetermined standard values. To increase efficiency in the use of beds, the hospital should evaluate the beds and improve the quality of service.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".