A descriptive analysis of medical health services utilization of Veterans living in Ontario: a retrospective cohort study using administrative healthcare data
Bibliographic record
Abstract
Abstract Background Health services utilization by Veterans following release may be different than the general population as the result of occupational conditions, requirements and injuries. This study provides the first longitudinal overview of Canadian Veteran healthcare utilization in the Ontario public health system. Methods This is a retrospective cohort study designed to use Ontario’s provincial healthcare data to study the demographics and healthcare utilization of Canadian Armed Forces (CAF) & RCMP Veterans living in Ontario. Veterans were eligible for the study if they released between January 1, 1990 and March 31, 2013. Databases at the Institute for Clinical Evaluative Sciences were linked by a unique identifier to study non-mental health related hospitalizations, emergency department visits, and physician visits. Overall and age-stratified descriptive statistics were calculated in five-year intervals following the date of release. Results The cohort is comprised of 23, 818 CAF or RCMP Veterans. Following entry into the provincial healthcare system, 82.6 % (95 % CI 82.1–83.1) of Veterans saw their family physician at least once over the first five years following release, 60.7 % (95 % CI 60.0–61.3) saw a non-mental health specialist, 40.8 % (95 % CI 40.2–41.5) went to the emergency department in that same time period and 9.9 % (9.5–10.3) were hospitalized for non-mental health related complaints. Patterns of non-mental health services utilization appeared to be time and service dependant. Stratifying health services utilization by age of the Veteran at entry into the provincial healthcare system revealed significant differences in service use and intensity. Conclusion This study provides the first description of health services utilization by Veterans, following release from the CAF or RCMP. This work will inform the planning and delivery of support to Veterans in Ontario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".