What is nursing work? A meta-narrative review and integrated framework
Bibliographic record
Abstract
BACKGROUND: There is ample evidence that modern nurses are under strain and that interventions to support the nursing workforce have not recognised the complexity inherent in nursing work. Creating a modern model of nursing work may assist nurses in developing workable solutions to professional problems. A new model may also foster cohesion among broad and diverse nursing roles. AIM: The aim of this meta-narrative review was to investigate how researchers, using different methods and theoretical approaches, have contributed to the understanding of nursing work. METHODS: A meta-narrative review was done to evaluate the trajectory of nursing work research, from 1953 to present. This review progressed through the stages of planning, searching, mapping, appraisal, and synthesis. FINDINGS: A total of 121 articles were included in this meta-narrative review. These articles revealed five narratives of nursing work, where work is conceptualised as labour. These narratives were physical labour (n = 14), emotional (n = 53), cognitive (n = 24), and organisational (n = 1), and combinations of more than one type of labour (n = 29 articles). The paradigms identified in the meta-narrative were the positivist, interpretive, critical, and evidence-based paradigms. Each article in the review corresponded with a paradigm and a labour narrative, creating a comprehensive model. CONCLUSIONS: Nursing work can be understood as a model of physical, emotional, cognitive, and organisational labour. These different types of labour may be hidden and taken for granted. Nurses can use this model to articulate what they do and how it supports patient safety. Nurses can also advocate for staffing allocations that consider all types of nursing labour. Tweetable abstract: Nursing work is complex and includes physical, emotional, cognitive, and organisational labour. Staffing needs to take all nursing labour into account.
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 | 0.045 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.031 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".