Bridging the digital literacy gap: Empowering patients and care partners through the Digital Sherpa program.
Bibliographic record
Abstract
e13716 Background: Digital literacy is vital for navigating modern healthcare, yet many older patients and care partners, especially in underrepresented communities, face barriers to accessing online resources. To address this, the Patient Empowerment Network (PEN) launched the digital sherpa™ program in 2016 and expanded it with Digitally Empowered™ in 2020, equipping 13,500 patients and families across the U.S. with digital skills for trusted information, telemedicine, and online support. At Moffitt Cancer Center, digital sherpa™ has supported 12,500 patients since 2018. Global partnerships in Cameroon, Canada, and Europe, with expansion to Lebanon in 2025, amplify its reach. Digitally Empowered™ offers self-paced modules, and the Train-the-Trainer program includes multilingual modules for scalability. Recognized with a Digital Health Award in 2021, the program advances health equity by reducing barriers to digital healthcare. Methods: The programs utilize a hybrid model of in-person and virtual workshops facilitated by trained volunteers and community partners. These workshops focus on building skills like accessing the internet, identifying credible health resources, using telemedicine, and leveraging social media. Key components include Train-the-Trainer workshops, ten video modules (in English and Spanish) and resource guides via Digitally Empowered™, and pre- and post-assessments to evaluate participant confidence, skill acquisition, and tool use. Results: In 2024, the program partnered with community-based organizations, nonprofits, and healthcare institutions to deliver workshops to 2,100 participants from underrepresented communities. Surveys showed 100% of participants found the program helpful and were willing to attend future sessions. Confidence in technology use increased from 22.2% pre-training to 55.56% post-training. Participants shared feedback such as, “ I used to be afraid of technology, but now I can video call my grandchildren and manage doctor’s appointments using my tablet.” Through these workshops, the program reduces barriers and fosters improved outcomes for patients and care partners. Conclusions: The digital sherpa™ and Digitally Empowered™ programs address critical digital literacy gaps among older patients and their care partners, empowering them to access tools to improve health outcomes. Scaling these initiatives through partnerships and multilingual modules bridges the digital divide and promotes health equity. References: Arora, Sanjeev, et al. “Leveraging digital technology to reduce cancer care inequities.” American Society of Clinical Oncology Educational Book , no. 42, July 2022, pp. 559–566, https://doi.org/10.1200/edbk_350151 Saeed, S. A., & Masters, R. M. (2021). Disparities in Health Care and the Digital Divide. Current psychiatry reports, 23(9), 61. https://doi.org/10.1007/s11920-021-01274-4 .
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.004 |
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".