Determinants of Health of People Living in the Community and Institutions
Bibliographic record
Abstract
Background and motivation: The identification of medical and non-medical factors that explain the heterogeneity in population health is an important topic in health policy. Previous determinants of health studies have mainly dealt with community samples. The few that have dealt with both community and institutionalized population are descriptive or focus of the risk of institutionalization. Results from our study will provide important evidence on the generalizability of the population health/determinants of health framework. Objectives: The primary objective is to test whether the same variables explain variations in health of people living in the community and in institutions. The secondary objective is to identify variables that have quantitatively important and statistically significant associations with health-related quality of life (HRQL) for those living in the community and institutions. Methodology: Data used for the analyses was 1996/97 National Population Health Survey household and institutions (combined) cross-sectional Microdata files. Target population was those aged 40 and over (sample size is approximately 37,000). A multiple linear regression model (overall Health Utilities Index Mark 3 (HUI3) scores as a dependent variable) with a dummy variable indicating living arrangement (community or institutions) was estimated to examine whether the same statistical equation holds for the community and the institutional data. Individual characteristics, socio-economic status, and health risk factors were included in the model as control variables. A Chow test was conducted for this purpose. In addition, the clinical importance and statistical significance of each coefficient were assessed. For HUI3, differences of 0.03 or more in overall scores are interpreted as quantitatively important. Sampling weights and Taylor linearization method were used to take account of the complex survey design such as unequal selection probabilities, stratification and clustering. SAS 9.0 and SUDAAN 9.0.1 were used for statistical analyses. Results: Chow test was significant (p Conclusions: Determinants of health equations importantly differ between community and institutional residents. Usual determinants of health factors such as age, education and financial status seem not to be major factors that explain the variations in health of residents in the institutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".