Developing an online health literacy curriculum for two German universities: a key stakeholder approach
Bibliographic record
Abstract
Health literacy is a significant resource for daily life in society. Global evidence reveals that there are less than ideal levels of health literacy in populations. One potential straproviding them with the skills and tools that will improve their knowledge and practice as our future workforce. The purpose of this study was to articulate the need to develop an online health literacy introductory course for university students in Germany. A total of 16 students from two German universities participated in focus group interviews to collect data on the extent of student health literacy awareness and related health and education needs. Nine international stakeholders participated in an online self-guided review of a comprehensive draft course to obtain detailed feedback from experts in the education and health literacy fields. Results revealed that both focus group and international stakeholders are in support of developing an online health literacy curriculum. To build the draft curriculum, an existing Canadian health literacy online course was adapted as a blueprint for the German context. The proposed course was customized based on the findings from the focus groups and international stakeholder feedback, which is intended to help inform and determine contents, design, and delivery of such a course applicable for universities in Germany and beyond.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".