Intensive Care Nurses' Belief Systems Regarding the Health Economics: A Focused Ethnography
Bibliographic record
Abstract
BACKGROUND: Health care beliefs can have an effect on the efficiency and effectiveness of nursing practices. Nevertheless, how belief systems impact on the economic performance of intensive care unit (ICU) nurses is not known. This study aimed to explore the ICU nurses' beliefs and their effect on nurse's practices and behavior patterns regarding the health economics. METHODS: In this study, a focused ethnography method was used. Twenty-four informants from ICU nurses and other professional individuals were purposively selected and interviewed. As well, 400 hours of ethnographic observations were used for data collection. Data analysis was performed using the methods described by Miles and Huberman (1994). FINDINGS: Eight beliefs were found that gave meaning to ICU nurse's practices regarding the health economics. 1. The registration of medications and supplies disrupt the nursing care; 2.Monitoring and auditing improve consumption; 3.There is a fear of possible shortage in the future; 4.Supply and replacement of equipment is difficult; 5.Higher prices lead to more accurate consumption; 6.The quality of care precedes the costs; 7. Clinical Guidelines are abundant but useful; and 8.Patient economy has priority over hospital economy. Maintaining the quality of patient care with least attention to hospital costs was the main focus of the beliefs formed up in the ICU regarding the health economics. CONCLUSIONS: ICU nurses' belief systems have significantly shaped in relation to providing a high-quality care. Although high quality of care can lead to a rise in the effectiveness of nursing care, cost control perspective should also be considered in planning for improve the quality of care. Therefore, it is necessary to involve the ICU nurses in decision-making about unit cost management. They must become familiar with the principles of heath care economics and productivity by applying an effective cost management program. It may be optimal to implement the reforms in various aspects, such as the hospital's strategic plan and supply chain management system.
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 | 0.005 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".