THE WORLD OF THE NURSE: “HERE WE NEVER REST”. CARE CONTEXT
Bibliographic record
Abstract
Objective: to understand the context, goals and meanings that nurses give to their care roles. Methodology: qualitative investigation whose methodological approach was focused ethnography in nursing care to surgical patients in high complexity health institutes in Medellín, Colombia. The study is performed between 2005 and 2007. The data collection is done by means of participant observations and in-depth interviews to nurses. We carry out a total of 11 nurses’ interviews and observations of shifts changes, nursing rounds, medical rounds and patients discharge. We use an informed consent form that each interviewee signed. In open coded analysis we meticulously examin and compare searching, differences and similarities. Results: the findings make clear that nurses consider patient first of all as human beings and they believe that humanity is an essential component for those who you look after and is a value for those who provide care. Attention to every detail makes part of the nurse’s world in their care actions. They are responsible for everything in the service and it implies “having to have four eyes”. Conclusion: the nurses’ world, in the studied services, is characterized as a complex context that demands to be permanently attentive to multiple and simultaneous situations. In their own words is defined as “Here we never rest”.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".