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Record W3049718480 · doi:10.1111/opn.12337

Nurses’ perceptions of their role in functional focused care in hospitalised older people: An integrated review

2020· review· en· W3049718480 on OpenAlexaff
Nicholas L. Swoboda, Sherry Dahlke, Kathleen F. Hunter

Bibliographic record

VenueInternational Journal of Older People Nursing · 2020
Typereview
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCINAHLNursingMEDLINEHealth careFunction (biology)Psychological interventionPerceptionMedicineAcute carePsychology

Abstract

fetched live from OpenAlex

AIM: The aim of this integrative review was to identify nurses' perspectives of their role in influencing the functional status of hospitalised older people. METHODS: An integrative review using Whittemore and Knafls' method was conducted using EBSCOhost CINAHL, Ovid MEDLINE(R), EBSCOhost, Social Gerontology, Cochrane Database of Systematic Reviews and ProQuest Dissertations & Theses data bases. Only studies with nurses' perspectives, or beliefs about their role in function-focused care were included. Content analysis was used to develop the themes nurses' role in function-focused care and barriers to functional care. RESULTS: The review found 12 relevant articles. Nurses believed that they were responsible for function-focused care, yet functional care tasks were often missed. Organisational contexts created many barriers to providing function-focused care for patients. Nurses felt powerless to address these overarching problems in their organisations. CONCLUSION: Nurses understand the importance of functional care yet often fail to carry out functional care interventions. Lack of organisational support creates a workplace that is short on staff, time and equipment and does not prioritise functional care needs. Nurse leaders and healthcare organisations need to reprioritise function-focused care for the good of patients, families and healthcare budgets.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.347
Teacher spread0.325 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations18
Published2020
Admission routes1
Has abstractyes

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