What are nurses’ roles in modern healthcare? A qualitative interview study using interpretive description
Bibliographic record
Abstract
Aim: Nursing work has historically been difficult to specify. This has led to difficulties in determining safe staffing requirements and adequately supporting safe patient care. The aim of this qualitative interview study was to explore how nurses understand their work. Design: Qualitative interview study, using the interpretive description methodology. Methods: Twenty registered nurses and nursing students completed semi-structured interviews about their work. The researcher drew on the interpretive description methodology to analyse interview data and create a model that interprets participants' experiences of their nursing work. Results: Nurses understand their work by its role in the healthcare system, rather than by the tasks or activities they complete. This understanding is significant because nurses adapt their work constantly, and rigid definitions of working would not support safe adaptation. Nurses report working across three broad roles: clinical work, which is patient-facing; managing work, which sustains the care environment; and enabling work, which provides supports like research and education that make nursing a profession. Conclusions: Clinical, managing and enabling work have different aims, but all serve the purpose of supporting safe patient care and sustaining healthcare systems. Adaptation is a constant feature of each of these roles. This model may be useful for nurses in structuring and explaining their work and informing nursing workforce policy.
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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 | 0.038 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".