SOCIAL AND ECONOMIC INEQUALITIES: THE MARGINALISATION OF OLDER PEOPLE
Bibliographic record
Abstract
This symposium will explore differing dimensions of social and economic inequalities and their impacts on the health and wellbeing of older citizens across differing countries and contexts. As the evidence concerning the impact of social relationships on morbidity and early death has grown in recent years, the challenge for researchers has been to understand its dimensions and the pathways that lead to positive rather than negative outcomes. Such research can be expected to have profound implications for positive health status and substantially reduced health and welfare budgets. The four presentations by researchers from Canada, New Zealand, Ireland, England and Poland will provide research data that addresses gender dimensions, urban and rural differences, multi-dimensional measurement constructs, social relationships, health, wealth, neighbourhood characteristics, area deprivation, discrimination and abuse to identify obvious and hidden aspects of the ways in which older people are often marginalised in their communities. These multiple domains of investigation will produce findings that can inform better quality service provision for older people and enable smart evidence based policy interventions to be developed. After attending this symposium, participants will have an informed understanding of the many forms of social and economic marginalisation and their negative impact on health and longevity. Additionally, participants will appreciate the multi-dimensional aspects of social and economic inequalities and ways to measure them. They will also be informed of risk and mitigating factors that will enhance the provision of services and evidence based policies that increase social inclusion for older citizens and reduce marginalisation.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".