Optimizing health promotion among ethnocultural minority older adults (EMOA)
Bibliographic record
Abstract
Purpose – The purpose of this paper is to identify the salient barriers in the uptake and effective utilization of health promotion interventions among ethnocultural minority older adults (EMOA). Design/methodology/approach – The paper opted for a literature review of 25 sources (peer-reviewed articles as well as documents from the grey literature). The search was primarily conducted in a database developed during a scoping review on the health and health care access and utilization of EMOA. Emphasis was placed on older ethnocultural minorities in Canada; however examples from the UK (which has a comparable health care system) and the USA and Australia (which have large, ethnically diverse populations) were also selected. The Candidacy framework was used as an analytical lens in the review. Findings – Findings indicate that health promotion needs to be understood as comprehensive care, involving not only the provision of health care services, but also knowledge dissemination and the facilitation of access to these services. Limited health literacy, low levels of self-efficacy and autonomy, and diverse life course experiences, particularly in the case of immigrant older adults, give rise to issues around the identification of need and system navigation. Cultural beliefs on health and illness, particularly around diet and exercise, and a lack of trust in formal systems of health care, are barriers to the uptake of interventions. Similarly, service permeability is low when cultural competency is lacking. Practical implications – The recommendations include the need for collaborative engagement with stakeholders, including family, peers, community partners and health practitioners, and the development of concise, culturally, and linguistically appropriate tools of health promotion that are targeted toward the intersecting needs of individuals in this diverse population of older adults. Originality/value – Given the increasingly diverse nature of the older adult population in Canada over the past four decades, this paper makes an important contribution toward understanding the social, cultural, structural, biographical, and geographical factors that may optimize the effective dissemination and uptake of health promotion interventions among EMOA.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".