Developing a measure for health professionals’ attitudes toward veterans
Bibliographic record
Abstract
U.S. veterans have complex healthcare needs that require professionals who are properly trained to address these issues. However, little is known about the attitudes that nurses and other professionals have toward veteran patients, particularly those working in community-based settings where it is unlikely training on veterans’ issues has occurred. Understanding health professionals’ attitudes toward caring for veterans is an important step in developing a workforce that is knowledgeable and willing to serve this complex and growing population. The purpose of this study was to develop and validate the Health Professionals’ Attitudes Toward Veterans (HPATV) scale, which explores attitudes regarding military cultural sensitivity and awareness, provision of care to veteran patients, and the prominent veterans’ health issues. The HPATV was developed across several phases, including review of existing measures and literature regarding veterans’ health and attitude structure, hypothesis of a factor structure, identification of a theoretical framework for attitude construction, item generation, 3-round Delphi survey to refine items and test content validity, piloting the measure among health professions students, and exploratory (EFA) and confirmatory factor analysis (CFA). Following CFA, the final 14-item scale revealed 3 latent factors to describe health professionals’ more nuanced attitudes toward working with veteran patients: culture, care, and health. The HPATV is a validated and readily available tool for needs assessment, quality improvement, and evaluation. Use of this tool will help increase understanding of these culture, care, and health domains and generate quality improvement initiatives based on them—ultimately benefiting veteran patients through more sensitive, patient-centered care.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".