Differences in Electronic Personal Health Information Tool Use Between Rural and Urban Cancer Patients in the United States: Secondary Data Analysis
Bibliographic record
Abstract
BACKGROUND: Studies have previously shown that rural cancer patients are diagnosed at later stages of disease. This delay is felt throughout treatment and follow-up, reflected in the fact that rural patients often have poorer clinical outcomes compared with their urban counterparts. OBJECTIVE: Few studies have explored whether there is a difference in cancer patients' current use of health information technology tools by residential location. METHODS: Data from 7 cycles of the Health Information National Trends Survey (HINTS, 2003-2017) were merged and analyzed to examine whether differences exist in managing electronic personal health information (ePHI) and emailing health care providers among rural and urban cancer patients. Geographic location was categorized using Rural-Urban Continuum Codes (RUCCs). Bivariate analyses and multivariable logistic regression were used to determine whether associations existed between rural/urban residency and use of health information technology among cancer patients. RESULTS: Of the 3031 cancer patients/survivors who responded across the 7 cycles of HINTS, 797 (26.9%) resided in rural areas. No difference was found between rural and urban cancer patients in having managed ePHI in the past 12 months (OR 0.78, 95% CI 0.43-1.40). Rural cancer patients were significantly less likely to email health care providers than their urban counterparts (OR 0.52, 95% CI 0.32-0.84). CONCLUSIONS: The digital divide between rural and urban cancer residents does not extend to general ePHI management; however, electronic communication with providers is significantly lower among rural cancer patients than urban cancer patients. Further research is needed to determine whether such disparities extend to other health information technology tools that might benefit rural cancer patients as well as other chronic conditions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".