Simplifying the care plan documentation procedure – An interview study with nurses at a medical ward at a university hospital in Sweden
Bibliographic record
Abstract
Objective: Swedish healthcare is experiencing an ongoing change from a biomedical perspective to person-centered care (PCC). Therefore, a transition in documentation of assessment, care and treatment is needed. The aim of this study was to describe nurses’ experiences with care plans at a university hospital medical ward in Western Sweden.Methods: Six semistructured interviews were conducted with nurses, and the data were analyzed using a qualitative content analysis with an inductive approach.Results: Nurses’ experiences with working with care plans were described as improving patient safety and included the following three subcategories: managing a high workload, collaboration improves documentation and creating structure in the medical records. In summary, nurses highlight a lack of time and team collaboration as important denominators in creating conditions for mutual care plans.Conclusions: Working with care plans is an important part of a nurse’s work. Procedure, use of documentation and ensuring regular revision all influence the quality of care due to the simple and clear structure of documentation within the medical record. To strengthen the patient’s involvement in a mutual care plan, nurses play a key role in implementing PCC, which is a tool used to improve partnerships between patients and health professionals.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".