Incorporating digital health into organizational health literacy: An updated definition, tools, and recommendations
Bibliographic record
Abstract
Health literacy is important from two perspectives: the individuals (personal health literacy) and the organizations providing information and services (organizational health literacy). While research has addressed digitalization in healthcare and associated barriers and enablers in personal health literacy (e.g., digital health literacy), these developments have not been paraleled in organizational health literacy. In this article, we proposed an augmented definition of organizational health literacy and conducted a gap analysis of the Health Literacy Universal Precautions Toolkit to expand it for digital health. Important advancements, specifically for virtual care, have been made, yet a broader approach must be adopted for all digital health technology. We proposed a series of modifications to emphasize the importance of digital health in organizational health literacy. Organizations must equitably enable individuals to understand and use digital information and services. In this monograph, we describe the current informatics gap and the required competencies, policies, and infrastructure to close the gap.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".