“Stepping up to the plate”: Identifying cultural competencies when providing health care to Canada’s military and Veteran families
Bibliographic record
Abstract
Introduction: Military family life is characterized by frequent relocations, regular periods of separation, and living with the persistent risk of injury or death of their military family member. The cumulative effects of these life events impact the health and wellness of military and Veteran families (MVFs) and may be exacerbated by challenges of accessing and navigating new health care systems when families relocate or when confronted with health care providers (HCPs) unaware of their experiences. Developing cultural competency in HCPs has been found to be beneficial to both the service provider and the service user. The purpose of this study is to identify cultural competencies for HCPs who work with MVFs. Methods: We completed a qualitative study using critical incident one-on-one interviews with HCPs. We used framework analysis for data analysis. Results: In total, we completed nine interviews with HCPs who have experience working with MVFs. Cultural competencies were identified in the domains of cultural awareness, cultural sensitivity, cultural knowledge, and cultural skills. Evidence also indicates the role of the ecological context on the ability of HCPs to be culturally competent. Discussion: Necessary competencies have been identified when providing culturally competent care to MVFs. The results highlight the need for MVF cultural competency training during pre-service health professional curricula and continuing education. We have acknowledged the need for policy and regulatory changes to facilitate the access and utilization of culturally informed health care. Finally, the cultural competencies identified will contribute to the development of an MVF cultural competency model for HCPs working in Canada.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".