Does a rural residence predict the development of depressive symptoms in older adults?
Bibliographic record
Abstract
OBJECTIVE: We sought to determine whether adults aged 65 years or older living in rural areas who are without depressive symptoms have a lower risk of developing depressive symptoms over 5 years than their urban counterparts, and to determine the factors that predict the development of depressive symptoms in older adults in rural and urban areas. METHODS: We conducted a secondary analysis of an existing data set, the Manitoba Study of Health and Aging (MSHA.) We studied a population-based random sample of 807 people without depressive symptoms or cognitive impairment who were residing in Manitoba communities in 1991/92 and 5 years later in 1996/97. We defined "rural" as a census subdivision with a population of less than 20,000, and "urban" as a population of 20,000 or greater. The MSHA investigators measured depressive symptoms using the Center for Epidemiologic Studies Depression scale, using the standard cut-point of 16 or more. Participants reported their age, sex, education, self-rated health, and functional status at the time of their first interview. RESULTS: Of adults aged 65 years or older living in urban areas, 13.3% developed depressive symptoms, versus 8.9% of those living in rural regions (p = 0.047). In multivariate analyses, a rural residence was not associated with the development of depressive symptoms. In rural areas, factors predicting depressive symptoms were female sex and poor self-rated health at the time of the first interview. CONCLUSION: A rural residence is only weakly protective for the development of depressive symptoms over 5 years, and this association was not seen after we accounted for potential confounding variables. As well, these results underscore the strong association between poor health and depressive symptoms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".