Literature Review on Rural–Urban Differences in Well-being after Transition to Civilian Life
Bibliographic record
Abstract
Rural residence has been associated with certain socioeconomic disadvantages, such as lower income levels and employment rates, which have been shown to be related to poorer health. These limitations may hold implications for veterans of the Canadian Forces (CF) because approximately 20% of CF veterans reside in rural regions. This study explores the literature on rural and urban differences in well-being among military veterans to determine whether one geographic group might be more at risk for difficulties in transition to civilian life. In the general population, urban dwellers tend to experience better physical health than their rural counterparts, but findings on mental health have been mixed. Veteran research has produced similar results, with urban veterans generally reporting better physical functioning, possibly due to reduced access to care. Research on the mental health of veterans, however, has not shown conclusive results, with some studies demonstrating an advantage for rural veterans in psychological well-being, and others indicating that veterans in rural regions are at an increased risk for mental health problems. The generalizability of the research is limited, however, by a lack of consistency in defining rural, and by a dearth of research on rural-urban differences among veterans of the CF.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".