IMPLEMENTASI DISCHARGE PLANNING PERAWAT RUANGAN DALAM UPAYA PENINGKATAN PENGETAHUAN PERAWAT
Bibliographic record
Abstract
Problems in Discharge Planning are still a problem in various countries; several efforts have been made by professional bodies and the Government in England, the United States, Australia and Canada to plan Optimal Discharge Planning. While the immediate impact of a lack of Discharge Planning is causing suffering for patients and their families and increasing costs to the health system, even small reductions in length of stay and hospital readmission rates can have a major financial impact.The methods used in writing this scientific paper are interviews, observation, implementation and evaluation. The head of the room attended this activity, 2 team leaders and eight executive nurses. It was found that the nursing management problem found was that the implementation of discharge planning in the Siger room could have been more optimal. The plan that will be carried out is education regarding discharge planning, with the target activity being nurses in the Siger room at the regional hospital of Dr. A. Dadi Tjokrodipo Bandar Lampung. This activity was carried out to increase the knowledge and abilities of nurses in carrying out discharge planning in the Siger room.Implementation was carried out on April 3rd, 2024, with the results that understanding of nursing discharge planning in the Siger room had increased, as evidenced by the results of the pre-test 60-75 and post-test 85-95. Therefore, it is expected that the head of the nursing department, the head of the room, and all nurses in the Siger room are committed to carrying out discharge planning consistently according to the Standard Operational Procedure because the accomplishment of discharge planning is a result of a nurse's role in carrying out the standard operational procedures that have been determined
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".