Hope of Patient Recovery in the ICU From the Viewpoint of Iranian Nurses: Concept Analysis
Bibliographic record
Abstract
Nurses' care quality for patients in the ICU depends on their degree/ level of hope to improving patient, but there is no consensus on the concept "hoping to improve patient." The purpose of the present study is to analyze the concept nurses hoping to improving patient in the ICU. To analyze this concept, hybrid model is used which consists of theoretical phase, field work phase, and final analytical phase. In field phase work, semi-structured, face to face and individual interviews were done for nurses working in the ICU, and the data gathered from the interviews were analyzed using inductive content analysis. In theoretical phase, the concept hoping to improve patient was characterized by being available, being professional, expecting positively, and being future- oriented. The scientific definition of this concept was explained through properties which are necessary for qualified nursing care. In field work phase, the categories include nursing care, inner feeling, belief and consequences. In final analytical phase, final definition of the concept was explained through properties such as dynamic expectation, being realistic, and being goal- oriented which is a better function and attitude in effective nursing care accompanying peace of mind for nurses. Concept analysis showed that nurse's awareness of hoping to improve patient helps the nurse do his job in the best way and with peace of mind.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| 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".