Determinants of chronic physical health conditions in Canadian Veterans
Bibliographic record
Abstract
Introduction: Limited information is available about the determinants of chronic health conditions of Veterans despite the increasingly perilous nature of military engagements in recent decades. Methods: Econometric analysis, using probit and negative binomial models, was conducted on the basis of data from a cross-sectional self-reported health survey of Canadian Veterans to investigate the determinants of musculoskeletal, respiratory, gastrointestinal, and cardiovascular health conditions; pain; and diabetes. Results: The results stress the role of military service–related factors in the increased likelihood of chronic physical health conditions in Canadian Veterans. Army Veterans had an increased probability of musculoskeletal (0.08, p ≤ 0.001) and gastrointestinal (0.05, p ≤ 0.001) conditions and pain (0.07, p ≤ 0.01). Veterans who were deployed had an increased risk of musculoskeletal conditions (0.08, p ≤ 0.001) and pain (0.06, p ≤ 0.001). In terms of non–service-related factors, the results confirm the role of obesity as a statistically significant determinant of chronic musculoskeletal, respiratory, and cardiovascular conditions; pain; and diabetes. Female Veterans were also at higher risk of respiratory and gastrointestinal conditions. Low-income Veterans have increased probability of musculoskeletal, gastrointestinal, pain, and cardiovascular conditions, and the risk decreased with rising income level. Finally, Veterans with mental health conditions had increased odds of musculoskeletal (OR = 2.79, p ≤ 0.001), respiratory (OR = 2.40, p ≤ 0.001), gastrointestinal (3.66, p ≤ 0.001), pain (OR = 2.61, p ≤ 0.001), and cardiovascular (OR = 1.45, p ≤ 0.01) conditions and diabetes (OR = 1.37, p ≤ 0.05). Discussion: The findings have important clinical and health resource use implications as Veterans seek treatment in community settings once they transition from military to civilian life. They also serve to advance the research agenda on the health of Veterans, an understudied population in Canada.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".