Health Literacy Education for Undergraduate Health Professions Students: A Call to Action
Bibliographic record
Abstract
The responsibility for producing safe and competent health care professionals rests with educators who periodically evaluate and modify curricula to meet program outcomes (Atcherson, Zraick & Hadden, 2017; Coleman, Peterson-Perry, & Bumsted, 2016; Kennard, 2016; Mosley & Taylor, 2017; Toronto & Weatherford, 2015). The association between client safety and effective communication is increasingly recognized in research (Wolf & Bailey, 2009). Communication strategies used to help clients understand and follow health teachings are fundamental to health literacy (HL) education (Agency for Healthcare Research and Quality, 2017). Recently, more health professions educators are integrating necessary HL concepts and evaluating the effectiveness of curricular efforts (Massey et al., 2017). Despite increasing integration of HL concepts into the curricula, teaching strategies and evaluation efforts vary greatly, in part due to the heterogeneity encompassed by the term “health professions education.” In the United States, the education continuum spans from the undergraduate (2-year and 4-year college degrees and pre-professional education; Campus Explorer, 2018) to the postgraduate or professional degrees of medicine, pharmacy, dentistry, and others. Because we teach in a 4-year nursing program, we use the term “undergraduate” to describe students who have not yet completed their baccalaureate degrees.
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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 | 0.045 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.031 | 0.032 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".