Workshifting – a tool to manage shortage of nurses? A content analysis at a medical ward at a university hospital in Sweden
Bibliographic record
Abstract
Background: Workshifting is a new model for redistributing tasks due to the shortage of health professionals such as registered nurses (RNs). Therefore, this study aim to describe registered nurses' experiences with workshifting in a medical ward at a university hospital in Sweden.Methods: Manifest qualitative content analysis with an inductive approach was used based on seven semi-structured interviews with RNs working in a medical ward at Sahlgrenska University Hospital in Gothenburg, Sweden.Results: One category, communication skills for increased cooperation, and three subcategories, Manage different skills, Changed work content and Lack of holistic nursing, were described. A healthy work environment (reasonable workload, interesting work tasks/content) is an important factor for attracting health professionals such as RNs.Conclusion: Workshifting redistributes tasks to added staff members such as pharmacists and assistant nurses, which decreases the RN workload; however, task-oriented work results in the lack of a holistic view of nursing. Moreover, enlarged teams need well-developed communication arenas to ensure patient safety and efficient work organization.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".