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Record W2965190025 · doi:10.5430/jha.v8n4p54

Simplifying the care plan documentation procedure – An interview study with nurses at a medical ward at a university hospital in Sweden

2019· article· en· W2965190025 on OpenAlexvenueno aff
Helena Larsson, Danijela Handanovic, Kristina Rosengren

Bibliographic record

VenueJournal of Hospital Administration · 2019
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersCentrum fÖr Personcentrerad VårdGöteborgs UniversitetSahlgrenska Universitetssjukhuset
KeywordsDocumentationWorkloadNursingHealth careMedical recordWork (physics)Quality (philosophy)MedicinePlan (archaeology)PsychologyMedical educationManagement

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.383
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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