Exploring the Relationship between Health Information Technology and Use of Prescribed Antihypertensive Medications
Bibliographic record
Abstract
Objective: We aimed to assess the correlation between various internet uses for health-related purposes and the utilization of prescribed antihypertensive medications. Additionally, we aimed to explore how socioeconomic status influences this relationship.
 Methods: This study was a cross-sectional analysis of 8,224 participants, representing 69,033,231 adults in the United States with hypertension who were prescribed antihypertensive medications. Out of these respondents, 7,837 individuals (88.8%) reported adherence to their prescribed medication regimen. The study examined several independent variables, including internet usage for (1) accessing health information, (2) filling prescriptions, (3) scheduling medical appointments, and (4) communicating with healthcare providers via email. The dependent variable under investigation was the usage of antihypertensive medications following prescription. Multiple logistic regression was employed to analyze the relationship between internet use for health-related purposes, adherence to prescribed antihypertensive medications, and the influence of socioeconomic status on this relationship. By utilizing this statistical approach, the researchers could assess the variables' associations while avoiding potential plagiarism issues.
 Results: After controlling for other factors, individuals who utilized the Internet for refilling prescription medications were found to have 1.65 times higher odds (95% CI 1.26, 2.16, p < .001) of taking prescribed antihypertensive medications compared to those who did not use the Internet for this purpose. Among hypertensive individuals who used the Internet for prescription refills, specific subgroups showed even higher odds of taking prescribed antihypertensive medications. Specifically, those who were employed had an adjusted odds ratio (AOR) of 2.04 (95% CI 1.39, 2.99, p < .001), college graduates had an AOR of 1.86 (95% CI 1.14, 3.04, p = .013). Individuals earning ≥ $20,000/year had an AOR of 2.74 (95% CI 1.68, 4.46, p < .001) compared to their unemployed counterparts, non-college graduates, or less than $20,000/year.
 Conclusion: The study suggests a potential association between online prescription refills and adherence to antihypertensive medications, with this relationship being particularly pronounced among individuals with higher socioeconomic status. Further research is warranted to explore the connection between health-related internet usage and medication adherence.
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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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".