Structural marginalization of older adults within health care settings: a concept analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Ageing populations present new challenges for society, governments and for health systems. To accommodate the global demographic shift towards older populations, healthcare systems preparedness is of paramount importance. To prepare and equip health systems to best support older adults' health and well-being over the long-term it is necessary to understand how current systems may unintentionally marginalize older adults. Therefore, the purpose of this review and analysis of the literature was to develop an operational definition of structural marginalization of older adults within healthcare settings. RESEARCH DESIGN AND METHODS: A concept analysis approach, which is a specialized type of literature review used to guide an in-depth examination of a word(s) to understand its importance, application and meaning, was used to review the literature published between January 2000 and December 2024. Eligible articles included those published in English, in peer-reviewed journals or grey literature, with a focus on how older adults experienced structural marginalization within health care settings. Older adults were defined as study participants who were identified as older adults by the study authors, where the lower age limit for inclusion was 60 years or where the study participants or population of interest had an average age of at least 60 years. Studies with participants who had conditions frequently associated with ageing such as dementia were also eligible for inclusion. RESULTS: The following defining attributes of structural marginalization of older adults within health care settings were determined through analysis of the 36 included articles: (a) health system actions bias and disadvantage accessibility to care, (b) insufficient organizational structure, composition, activities, and actions undermine older adults' health, (c) policies fail to protect or support, (d) care is reduced or compromised, and (e) the social determinants of health influence access and experience with healthcare organizations. DISCUSSION AND IMPLICATIONS: The contributors to structural marginalization of older adults within healthcare are complex and multifaceted. Successful resolution of this widespread concern requires a collaborative approach that includes recognition of the issue, targeted education, financial resources, and action towards resolution across all levels within healthcare systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".