How does the health status of older migrants compare to the Canadian and Australian-born population?
Bibliographic record
Abstract
Background: Despite Australia’s and Canada’s rich migration history and the well-known challenges posed by an ageing heterogenous migrant population, little attention has been paid to the health of older migrants in research, policy and practice.Aims and methods: My thesis investigated the variations in the health status of older migrants and their host population and its subsequent determinants using three study designs: systematic literature review (Australia, Canada); serial cross-sectional analyses of a combined dataset (Dynamic Analyses to Optimise Ageing (DYNOPTA), Australia); longitudinal analysis of a DYNOPTA contributory study (Household Income and Labour Dynamics in Australia Study (HILDA)).Findings: In general, the systematic review found older migrants reported an objective health advantage for some non-communicable diseases, but a disadvantage for infectious diseases and poor mental health relative to the older Australian and Canadian-born population. Health (dis)advantages varied by region/country of birth, age, sex and migrating circumstances.With regards to self-reported health, neither the systematic review nor the repeated cross- sectional analysis found convincing differences using binary country of birth. However, using region of birth sub-groups the systematic review and longitudinal analysis demonstrated a self-rated health advantage in North-West Europeans and a self-rated health disadvantage in Southern and Eastern Europeans – both of relevant magnitude. Longitudinally, being older, divorced or never married, current or former smoker and first, native or preferred language other than English were associated with poor health. Higher education attainment, alcohol consumption and being female were associated with better self-rated health. Language, education and increasing age showed a “dose-dependent” association with self-reported health.Conclusions: My findings provide evidence that older migrants with cumulative education and language disadvantages – both potentially remediable - experience poorer self-rated health. In addition to economic integration, policies should address these issues with regard to their impact on health literacy and health inequalities, which persist and magnify as the migrant becomes older.
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".