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Record W4414255169 · doi:10.1002/nop2.70215

Stigma Towards Hospitalised Older Adults: A Concept Analysis

2025· review· en· W4414255169 on OpenAlexaff
Sadaf Murad‐Kassam, Wai Ching Yan, Rachel G. Khadaroo

Bibliographic record

VenueNursing Open · 2025
Typereview
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of AlbertaUniversity of Alberta Hospital
Fundersnot available
KeywordsStigma (botany)Formal concept analysisContext (archaeology)Health careConceptual frameworkSelf-concept

Abstract

fetched live from OpenAlex

AIM: The aim of this concept analysis paper is to explore the concept of stigma towards hospitalised older adults and propose a clear definition to understand this phenomenon. DESIGN AND REVIEW METHOD: Rodgers' evolutionary concept analysis method was used to evaluate the concept of stigma towards hospitalised older adults by identifying attributes, antecedents, and consequences. DATA SOURCES: A systematic search was conducted using CINAHL, PubMed, Scopus, and Google Scholar databases. Seventeen research articles from 1963 to 2025 were identified as directly related to the concept of stigma towards hospitalised older adults. RESULTS: Discrimination based on age, discriminatory practices, and negative stereotypes were the common attributes highlighted in research studies. The primary antecedent of stigma in hospitalised older adults is a social stigma which leads to stigmatised attitudes and practices towards older adults admitted into hospital. Inequalities in the hospital environment and lack of motivation are consequences that may provoke a stigmatised demeanour towards older patients in hospitals. CONCLUSION: A clear understanding of stigma in the context of hospitalised older adults will guide the development of a conceptual framework and improve the healthcare professionals' care approach towards older adults in the hospital setting.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.917
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.077
GPT teacher head0.506
Teacher spread0.429 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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