Multimorbidity, eHealth and implications for equity: a cross-sectional survey of patient perspectives on eHealth
Bibliographic record
Abstract
OBJECTIVE: There is increasing awareness of the burden of medical care experienced by those with multimorbidity. There is also increasing interest and activity in engaging patients with chronic disease in technology-based health-related activities ('eHealth') in family practice. Little is known about patients' access to, and interest in eHealth, in particular those with a higher burden of care associated with multimorbidity. We examined access and attitudes towards eHealth among patients attending family medicine clinics with a focus on older adults and those with polypharmacy as a marker for multimorbidity. DESIGN: Cross-sectional survey of consecutive adult patients attending consultations with family physicians in the McMaster University Sentinel and Information Collaboration practice-based research network. We used univariate and multivariate analyses for quantitative data, and thematic analysis for free text responses. SETTING: Primary care clinics. PARTICIPANTS: 693 patients participated (response rate 70%). INCLUSION CRITERIA: Attending primary care clinic. EXCLUSIONS: Too ill to complete survey, cannot speak English. RESULTS: The majority of participants reported access to the internet at home, although this decreased with age. Participants 70 years and older were less comfortable using the internet compared with participants under 70. Univariate analyses showed age, multimorbidity, home internet access, comfort using the internet, privacy concerns and self-rated health all predicted significantly less interest in eHealth. In the multivariate analysis, home internet access and multimorbidity were significant predictors of disinterest in eHealth. Privacy and loss of relational connection were themes in the qualitative analysis. CONCLUSION: There is a significant negative association between multimorbidity and interest in eHealth. This is independent of age, computer use and comfort with using the internet. These findings have important implications, particularly the potential to further increase health inequity.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".