The development and validation of a cultural competency model for health care providers working with military and Veteran families in Canada
Bibliographic record
Abstract
Objectives. Canadian military and Veteran family life is characterized by frequent relocation, regular familial separation due to training or deployment, and living with the risk of injury or death of a military family member. Most Canadian health care providers lack knowledge and skills to address these issues. Therefore, the purpose of the thesis was to develop and validate a cultural competency model for health care providers working with military and Veteran families. Methods. The thesis consisted of three phases. Phase One was a scoping review of health care literature to identify commonalities in the content, structure, and development of cultural competency models. Phase Two used competency framework development methodology to create a military and Veteran family model and framework. Data collection included interviews with military and Veteran family members (n=17) and health care providers (n=9). In Phase Three, a validation study was conducted of the proposed model using focus groups and interviews with military subject matter experts (n=12) and health care providers (n=8). Results. Based on population-specific data, the Military and Veteran Family Cultural Competency Model and framework was created that includes 24 cultural competencies articulated across four cultural competency domains. Conclusion. The thesis has identified and validated cultural competencies for health care providers working with military and Veteran families within the Canadian health care landscape. This validated model may help inform health provider education and professional development programs to increase awareness, knowledge, and skills to enhance the health experiences and health outcomes of military and Veteran families.
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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 | 0.055 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".