The Digital Divide Amongst High-Need High-Risk Veterans
Bibliographic record
Abstract
Abstract High-need high-risk (HNHR) veterans are medically complex and at the highest risk of hospitalization and long-term institutionalization. Technology can mitigate challenges these veterans have in accessing healthcare. Willingness to use technology as well as access and ability to use technology were assessed in this study. At the time of the survey, 2543 Miami VAHS veterans were listed as HNHR. 634 veterans ultimately completed the questionnaire, and 602 answered the “willingness to use video-visits” question. Of the 602 respondents, 327 (54.3%) reported they were willing for video-visits with the VA, while 275 (45.6%) were not. Those who were willing were significantly younger (P<0.001), with higher educational qualifications (P=0.002), and more health literate than those not willing (P<0.001). They were more also capable of using the Internet, more likely to use email and be enrolled in the VA’s patient portal, My HealtheVet (P<0.001). However, of the veterans who were willing, 248 (75.8%) had a device with video-capable technology. Those with video-capable technology were younger (P=0.004), more health literate (P=0.01), and less likely to be Black or African American (P=0.007). They were more capable of using the Internet, more likely to use email, and be enrolled in My HealtheVet than those without (P<0.001). Half of the respondents were willing for video-visits but a quarter of those willing lacked requisite technology, thereby making only about 41.2% of the respondents willing and video-capable. To minimize the digital divide, especially during the ongoing COVID-19 pandemic, targeted measures need to address these disparities in this vulnerable population.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".