How Do Scholars Conceptualize and Conduct Health and Digital Health Literacy Research? Survey of Federally Funded Scholars (Preprint)
Bibliographic record
Abstract
BACKGROUND The concept of health literacy (HL) is constantly evolving, and social determinants of health (SDoH) have been receiving considerable attention in public health scholarship. Since a 1-size-fits-all approach for HL fails to account for multiple contextual factors and as a result poses challenges in improving literacy levels, there is a need to develop a deeper understanding of the current state of HL and digital health literacy (DHL) research. OBJECTIVE This study examined scholars’ conceptualization and scope of work focused on HL and DHL. METHODS Using a search string, investigators (N=2042) focusing on HL, DHL, or both were identified from the grantee websites of the National Institutes of Health RePORTER (RePORT Expenditures and Results) and the Canadian Institutes of Health Research. The investigators were emailed a survey via Qualtrics. Survey questions examined the focus of work; whether the investigators studied HL/DHL in combination with other SDoH; the frameworks, definitions, and approaches used; and research settings. We analyzed survey data using SPSS Statistics version 28 and descriptive analysis, including frequencies and percentages, was conducted. Chi-square tests were performed to explore the association between the focus of work, settings, and age groups included in the investigators’ research. RESULTS A total of 193 (9.5%) of 2042 investigators responded to the online survey. Most investigators (76/153, 49.7%) were from public health, 83/193 (43%) reported their research focused on HL alone, 46/193 (23.8%) mentioned DHL, and 64/193 (33.2%) mentioned both. The majority (133/153, 86.9%) studied HL/DHL in combination with other SDoH, 106/135 (78.5%) conducted HL/DHL work in a community setting, and 100/156 (64.1%) reported not using any specific definition to guide their work. Digital tools (89/135, 65.9%), plain-language materials (82/135, 60.7%), and visual guides (56/135, 41.5%) were the top 3 approaches used. Most worked with adults (131/139, 94.2%) and all races and ethnicities (47/121, 38.8%). CONCLUSIONS HL and DHL research largely considered SDoH. Multiple HL tools and approaches were used that support the examination and improvement of literacy and communication surrounding health care issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.120 | 0.255 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".