An Interprofessional Education Project to Address Veterans’ Healthcare Needs
Bibliographic record
Abstract
Background/Objective: The number of veterans and their families seeking healthcare and support within civilian communities is increasing worldwide. There is a need for healthcare providers to provide sensitive, comprehensive care for veterans with both physical and behavioral health conditions. Many civilian providers are unfamiliar with veterans’ issues and need training on military culture and combat experiences in order to provide compassionate, high quality care. An interprofessional (IPE) course to increase health professional students’ understanding of military culture and the associated health problems of veterans was implemented and evaluated. Methods: An 8-week IPE immersion course was offered for students with clinical experience at a Veterans’ Health primary care clinic and a didactic component. The class content included military culture, behavioral and physical health disorders common among veterans, and the related behavioral and pharmacological treatments. Faculty-led discussions with students in IPE teams used veteran-focused case studies and standardized patients to prepare students to work in IPE teams in the clinical care of veterans. Results: This educational project was evaluated using quantitative surveys and qualitative reflection questions and focus groups. Students scored high for readiness for interprofessional learning pre-course. Post-course students reported valuing the team approach to veterans care and students engaged in high levels of communication and collaboration within the team. Students’ knowledge scores increased related to understanding of military culture and their patient advocate role. Conclusions: Students learned about military culture and the provision of humanistic, high quality care for military veterans in this clinical and didactic immersion IPE course.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".