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Record W2100338967 · doi:10.1177/1744987106065684

Observations of the experiences of people with dementia on general hospital wards

2006· article· en· W2100338967 on OpenAlexfundno aff
Rachel Norman

Bibliographic record

VenueJournal of research in nursing · 2006
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
FundersAlzheimer Society
KeywordsDementiaVariety (cybernetics)NursingAffect (linguistics)Identity (music)MedicinePsychologyRealisationDiseaseCommunication

Abstract

fetched live from OpenAlex

This paper is based on research that aimed to explore how people with dementia are cared for in general hospital wards in the United Kingdom (UK). The paper details findings from one phase of data collection, ward based observations. The observations elucidated the ways in which persons with dementia express and portray their ‘selves’, the interpretations made by nurses about the patients with dementia they cared for and the constructions of roles and care environments. The findings demonstrate how a variety of influences affect the way a person with dementia experiences a hospital admission. Nurses' positive or negative interpretations of a person with dementia can lead to the ‘constraint’ or ‘realisation’ of a person's portrayal of self. In an ageing global society, improving the care of older people is a priority. The findings illuminate the central importance of promoting two-way relationships in which the actions of people with dementia are recognised as portraying individuality and identity. The study highlights a need for practice development to enhance nursing care for medical and surgical patients with a coincidental diagnosis of dementia.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0020.002
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.464
Teacher spread0.384 · 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 source (direct Gemma or distilled Codex), 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

Citations43
Published2006
Admission routes1
Has abstractyes

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