Social Determinants of Health in Digital Health Policies: an International Environmental Scan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Social Determinants of Health (SDoH) include factors such as economic stability, education, social and community context, healthcare access, and the physical environment, which shape an individual's health and well-being. Given that the inclusion of SDoH factors is essential in improving the quality and equity of digital health, this study aims to examine how SDoH is incorporated within digital health policies internationally. METHODS: An environmental scan of digital health policies was conducted, including relevant documents from multiple countries and global organizations. Key content related to SDoH was extracted from the documents, and a content analysis was conducted to identify seven different SDoH domains (i.e., target audience, SDoH inclusion, addressing health inequities, SDoH-related key performance indicators, data collection on SDoH, interoperability standards, and data privacy and security). Data were aggregated at the global and continental levels to integrate and synthesize information from different countries and regions. RESULTS: A total of 28 digital health policies or strategies were identified across 16 international regions. The comparative analysis of health policies regarding SDoH reveals a pronounced disparity between the continental regions. Although the World Health Organization recognizes the significance of key performance indicators for monitoring SDoH and emphasizes the assessment of national digital health maturity, there's a noticeable lack of continent-specific policies reflecting these global initiatives at the continental level. CONCLUSION: While some regional digital health strategies recognize SDoH, integration varies, and standardization is lacking. Future research should focus on data collection frameworks and comprehensive insights for policymakers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it