Stigma Towards Hospitalised Older Adults: A Concept Analysis
Bibliographic record
Abstract
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 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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".