Bridging the Telehealth Digital Divide With Collegiate Navigators: Mixed Methods Evaluation Study of a Service-Learning Health Disparities Course
Bibliographic record
Abstract
BACKGROUND: Limited digital literacy is a barrier for vulnerable patients accessing health care. OBJECTIVE: The Stanford Technology Access Resource Team (START), a service-learning course created to bridge the telehealth digital divide, trained undergraduate and graduate students to provide hands-on patient support to improve access to electronic medical records (EMRs) and video visits while learning about social determinants of health. METHODS: START students reached out to 1185 patients (n=711, 60% from primary care clinics of a large academic medical center and n=474, 40% from a federally qualified health center). Registries consisted of patients without an EMR account (at primary care clinics) or patients with a scheduled telehealth visit (at a federally qualified health center). Patient outcomes were evaluated by successful EMR enrollments and video visit setups. Student outcomes were assessed by reflections coded for thematic content. RESULTS: Over 6 academic quarters, 57 students reached out to 1185 registry patients. Of the 229 patients contacted, 141 desired technical support. START students successfully established EMR accounts and set up video visits for 78.7% (111/141) of patients. After program completion, we reached out to 13.5% (19/141) of patients to collect perspectives on program utility. The majority (18/19, 94.7%) reported that START students were helpful, and 73.7% (14/19) reported that they had successfully connected with their health care provider in a digital visit. Inability to establish access included a lack of Wi-Fi or device access, the absence of an interpreter, and a disability that precluded the use of video visits. Qualitative analysis of student reflections showed an impact on future career goals and improved awareness of health disparities of technology access. CONCLUSIONS: Of the patients who desired telehealth access, START improved access for 78.7% (111/141) of patients. Students found that START broadened their understanding of health disparities and social determinants of health and influenced their future career goals.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".