Enablers and challenges of caring in the Intensive Care Unit--Part 2: In relation to nurses
Bibliographic record
Abstract
The concept of caring is vague and complex, especially in critical environments such as the intensive care unit (ICU), where technological dehumanisation is a challenge for nurses. ICU nursing care includes not only patients but also extends to patients’ families, nurses, other health team members and the unit’s environment. Caring in critical care settings is affected by enabling and impeding factors. To explore these enablers and challenges factors, a focused ethnographic study was conducted in an Australian ICU. The data was collected from 35 registered nurses through various resources: participants' observations, documents reviews, interviews, and additional participants’ notes. Data were analysed inductively and thematically. The study outlines comprehensively and widely a wide range of enablers and challenges affecting caring in the ICU - which originate from different sources such as patients, families, nurses, and the ICU environment. This paper is the second in a two-part series which explores the ICU nurses’ experiences and perspectives of the enablers and challenges of caring in the ICU. Part 1 was concerned with the enablers and challenges to caring that are related to ICU patients, families, and environment. While Part 2 introduces readers to the enablers and challenges factors that are concerned with the nurses in ICU. These factors include nurses’ educational backgrounds and professional experience, employment working factors, leadership styles, relationships, and personal factors. Nurses and other stakeholders such as clinicians, educators, researchers, managers, and policymakers need to recognize these factors and their implications for providing quality care, in order to enhance and maintain the optimal level of caring in the ICU.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".