Exploring Veteran cultural competency in Canadian health care services
Bibliographic record
Abstract
Introduction: Thousands of Canadian Armed Forces (CAF) Veterans leave service and transition to civilian life each year. This involves leaving the military health care system and transitioning to civilian provincial or territorial health care systems. Emerging research has shown that increased awareness of military culture - the culture from which Veterans are coming - among health care providers positively impacts the quality of health care delivery. The objective of this study was to identify Veteran cultural competencies to enhance and support the transition to civilian health care services. Methods: In this qualitative study, critical incident technique (CIT) was used to conduct individual interviews. Purposive sampling was used to recruit Regular Force Veterans and health care professionals who worked with Veterans. Data analysis was conducted using framework analysis. Results: Eight interviews were completed, including five Veterans, two physicians, and one occupational therapist. Identified competencies resulted in a framework representing four cultural competency domains: awareness (5 competencies), sensitivity (5 competencies), knowledge (8 competencies), and skills (10 competencies). Discussion: The study identified military cultural competencies specific to the health care experiences for Canada's Veterans and validated existing cultural competency domains. Themes identified can be used to inform the development of clinical reference guides, including advice for health care providers to build knowledge when working with Veterans. In addition, resources can be created to support Veterans as they prepare to leave military service and begin accessing the civilian health care system.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".