Expanding a Behavioral View on Digital Health Access: Drivers and Strategies to Promote Equity
Bibliographic record
Abstract
The potential and threat of digital tools to achieve health equity has been highlighted for over a decade, but the success of achieving equitable access to health technologies remains challenging. Our paper addresses renewed concerns regarding equity in digital health access that were deepened during the COVID-19 pandemic. Our viewpoint is that (1) digital health tools have the potential to improve health equity if equitable access is achieved, and (2) improving access and equity in digital health can be strengthened by considering behavioral science-based strategies embedded in all phases of tool development. Using behavioral, equity, and access frameworks allowed for a unique and comprehensive exploration of current drivers of digital health inequities. This paper aims to present a compilation of strategies that can potentially have an actionable impact on digital health equity. Multilevel factors drive unequal access, so strategies require action from tool developers, individual delivery agents, organizations, and systems to effect change. Strategies were shaped with a behavioral medicine focus as the field has a unique role in improving digital health access; arguably, all digital tools require the user (individual, provider, and health system) to change behavior by engaging with the technology to generate impact. This paper presents a model that emphasizes using multilevel strategies across design, delivery, dissemination, and sustainment stages to advance digital health access and foster health equity.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".