Internet use by end‐stage renal disease patients
Bibliographic record
Abstract
Information on the prevalence and predictors of use of the Internet by patients can be applied to the design and promotion of healthcare Internet technologies. To our knowledge, few studies on Internet use by end-stage renal disease (ESRD) patients have been reported. The objectives of this study are to ascertain the prevalence and predictors of Internet use by ESRD patients among different dialysis modalities. A questionnaire surveying Internet use was delivered in person to 199 conventional hemodialysis patients (57 returned), and mailed to 170 peritoneal dialysis (PD) patients (42 returned), and 65 nocturnal home hemodialysis (NHD) patients (43 returned). Of the respondents, most (58%) have used the Internet to find information on their health condition. The strong majority (76%) of these patients have easy access to the Internet. A higher proportion of NHD patients (86%) used the Internet compared with the PD patients (60%) (p=0.02). Internet use was found to be more prevalent with younger (p<0.001), more educated (p=0.001), and Canadian-born patients (p=0.005). The high prevalence of Internet use and easy access to the Internet by ESRD patients suggest that future Internet information and communication systems for healthcare management in ESRD will likely be well adopted by this patient 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".