Implications of Telemedicine on Educational Outcomes and Healthcare Accessibility, Case Study of a Local College in Upstate New York
Bibliographic record
Abstract
Abstract Importance There is a dearth of empirical research addressing the healthcare component on the academic performance of college students. It is unknown how discrepancies in healthcare access manifest in college students. Objective To improve awareness of the current healthcare issues facing college students and find ways to minimize the healthcare gap. Design, Setting, and Participants Local college students were assessed for health status, usage of the health system, potential factors discouraging the use of health benefits, and the educational outcomes of the students. Main Outcomes and Measures Educational outcomes associated with differing levels of healthcare access, averages, Pearson’s r, odds ratios and 95% CIs. Results Nearly 1 in 4 students (26.1% [SE, 3.7%]) reported having inadequate healthcare access. Healthcare accessibility was strongly correlated with academic performance (r = .336 [95% CI, .179-.476]; P< .001). Low access students were significantly more likely to report barriers to healthcare (OR = 2.91 [95% CI, .557-5.29]; P< .001). Telemedical use corresponded with reduced absences (r = .219 [95% CI, .054-.373]; P= .01) and higher ratings on self-health assessments (OR = 1.74 [95% CI, .809-3.75]; P= .001). Use of telemedicine did not relate to reports of healthcare barriers ( P >.99). Though the adoption of telemedicine among college students is staggeringly low, with fewer than 1 in 12 students (7.9% [SE 2.4%]) reporting at least some telemedical usage. Conclusions and Relevance Over a quarter of college students report inadequate healthcare access despite fewer than 1 in 12 students utilizing telemedicine. Further research is needed to determine the extent to how much the current healthcare norms affect college students, but fostering a pedagogical approach to telemedicine may serve to bridge the healthcare gap. Key Points Question In what way does healthcare accessibility affect college students? Findings Many students report inadequate healthcare access, despite having health insurance. The adoption of telemedicine among college students is currently low. Students utilizing telemedicine display improvements across health and academic categories. Meaning Bolstering the adoption of telemedical health services may help to diminish the healthcare accessibility gap among college students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".