Capacity and willingness of patients with chronic noncommunicable diseases to use information technology to help manage their condition: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Health care providers have shown considerable interest in using information technologies such as email, text messages and video conferencing to facilitate the management of chronic noncommunicable diseases such as hypertension, diabetes mellitus and vascular disease. We sought to determine whether these technologies are available and appealing to the target population. METHODS: We analyzed cross-sectional data from a computer-assisted telephone survey, conducted by Statistics Canada in February and March 2012, of western Canadian adults with at least 1 chronic condition. Survey respondents were asked about their capacity (e.g., "Do you own a mobile phone?") and willingness to use each of 3 information technologies (email, text messages and video conferencing) to interact with health care providers. For all analyses, Statistics Canada's calibrated design weights and bootstrap weights were used to obtain population-level point estimates for proportions and odds ratios. RESULTS: In total, 1849 (79.8%) of 2316 eligible people participated. Of the 1849 participants, 81.9% had hypertension, 26.2% had diabetes, 21.4% had heart disease, and 7.9% had stroke; 32.2% had more than 1 of the 4 chronic conditions of interest. High proportions of respondents owned a computer with Internet access (76.4%, 95% confidence interval [CI] 73.3%-79.3%) or a mobile phone (73.9%, 95% CI 70.7%-76.8%). About two-thirds of respondents were interested in using email to interact with a specialist (66.3%, 95% CI 63.0%-69.5%); respondents were less enthusiastic about using text messages (44.9%, 95% CI 41.2%-48.7%). Enthusiasm for video conferencing was more pronounced among those residing further from medical specialists than among those living closer. Among respondents who were potentially interested in video conferencing, almost 50% of remote dwellers would use this technology if it saved more than 60 minutes of travel time. INTERPRETATION: Many people were interested in using electronic technologies, especially video conferencing and email-based methods, to help manage their chronic condition. The effectiveness and cost implications of using email and video conferencing in the management of chronic disease deserve further consideration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".