Statistical modelling of mental distress among rural and urban seniors
Bibliographic record
Abstract
The senior population is growing rapidly in Canada. Consequently, there will be an increased demand for health care services for seniors who have mental illness. Seniors are more likely to live in rural areas than younger people; therefore, it is important to identify the differences between rural and urban seniors in order to design and deliver mental health services. The main objective of this paper was to use the National Population Health Survey (NPHS) to examine the differences with regard to mental distress between rural and urban seniors (i.e. 55 years and older). The other objectives were to investigate the long-term association between smoking and mental health and the long-term association between unmet health care needs and the mental health of seniors in rural and urban areas. The mental distress measure was examined as a binary outcome. The analysis was conducted using a generalized estimating equation approach that accounted for the complexity of a multi-stage survey design. Rural seniors reported a higher proportion of mental distress [OR=1.16; 95% CI: 0.98, 1.37] with a borderline statistical significance than urban seniors. This finding was based on a final multivariate model to study the relationship between mental distress and location of residence(i.e. rural or urban) as well as between smoking and self-perceived unmet health care needs, adjusting for other important covariates and missing outcome values. A significant correlation was noted between smoking and mental health problems among seniors after adjusting for other covariates [OR = 1.26; 95% CI: 1.00, 1.60]. Participants who reported self-perceived unmet health care needs reported a higher proportion of mental distress [OR = 1.72; 95% CI: 1.38, 2.13] compared to those who were satisfied with their health care.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".