Do Physician Assistant Training Programs Adequately Prepare PAs to Address Nutritional Issues in Clinical Practice?
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to determine physician assistants' (PAs') current level of confidence to engage in nutrition-related tasks and their satisfaction with the nutrition education they received in PA school. METHODS: To achieve this goal, a mixed-methods approach that consisted of 3 data collection phases (qualitative online discussions, quantitative survey, and qualitative interviews) was used to explore and measure PAs' perceptions of the education they received in PA school and through other sources and how confident they felt addressing nutrition-related issues in clinical practice. RESULTS: While 80% of PAs endorse the idea that PAs should be more involved in providing nutritional care to patients, the majority reported basic or no knowledge of the nutritional implications of chronic conditions (69%), inflammatory bowel disease (69%), nutritional needs over the lifespan (67%), and food allergies and intolerances (64%). Barriers to patient care included knowledge-related challenges when selecting lab tests based on patient profile (46%) and identifying needs based on various gastrointestinal diseases (67%) and when using diagnostic data to identify deficiencies (74%). Overall, 59% of PAs reported being slightly or very dissatisfied with the nutrition-related content in the curricula used to formally train PAs. CONCLUSIONS: The primary goal of every PA program is to prepare its graduates to be competent to enter clinical practice. Regarding nutrition, these data indicate that programs are failing to do so. PAs lack the confidence and ability to provide optimal nutritional care, which is staggering considering that nutrition is the first line of treatment in the prevention and management of numerous chronic diseases.
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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 | 0.006 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".