Access to health care and medical health services use for Canadian military families posted to Ontario: a retrospective cohort study
Bibliographic record
Abstract
Introduction: Canadian military spouses have reported issues accessing and maintaining high quality health care. There is no Canadian research quantifying the scope of the problem. Methods: This is a retrospective cohort study using administrative data. We included military spouses and dependents relocated to Ontario between January 8, 2008 and March 31, 2013, along with a matched civilian reference group. We measured hospitalizations, emergency department (ED) visits, and physician visits. Comparisons of first health care contact, medical health services use, and time to first health services use controlled for age, sex, and geography. Results: The cohort included 7,508 military family members and 30,032 matched civilians. Point of first health care system contact differed between military family members and the civilian reference group ( p < 0.001). Military family members had a longer time to their first health care contact than the civilian reference group (median 118 days vs. 84 days, p < 0.001). Similarities and differences between military family and civilian health services use existed. For example, military children and youth were less likely to see a paediatrician than the civilian reference group (17.7% vs. 26.0%, p < 0.001), and less likely to receive non-influenza vaccinations (23.2% vs. 32.3%, p < 0.001). Discussion: This study provides evidence supporting the hypothesis that military families have different access to, and use of, provincial health services than the general Ontario population and suggests support during relocations is needed. It is important to further understand how these patterns impact health outcomes and continuity of care and to contextualize these findings with potential differences in the underlying need for health services.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".