Perceptions of chronically ill and healthy consumers about electronic personal health records: a comparative empirical investigation
Bibliographic record
Abstract
OBJECTIVE: To develop a model of consumer perceptions of electronic personal health records (PHRs) and validate it in a comparative study between consumers who report having a chronic illness and those who report being well. MATERIALS AND METHODS: A model of PHR use motivators and barriers was built and tested through a national survey across Canada. Data were collected from 800 individuals, 18 years or older. Half reported having a chronic illness or disability and half reported being well. Analyses were performed with structural equation modelling techniques. RESULTS: A total of 389 answers from chronically ill and 383 from well participants were collected. Perceived usefulness was the key explanation of the intention to use PHRs for both ill and well people (total effect of 0.601 and 0.565, respectively) followed by security, privacy and trust in PHRs (total effect of 0.377 and 0.479, respectively). Conversely, computer anxiety was perceived as a significant barrier (total effect of -0.327 for ill individuals and -0.212 for well individuals). DISCUSSION: The model proposed was appropriate in explaining key consumer positive and negative perceptions on electronic PHR use. We found little difference in perceptions of electronic PHRs between chronically ill and well individuals, although self-reporting their health status might have influenced the results. CONCLUSIONS: To increase the adoption rate of electronic PHRs among both chronically ill and well consumers it is necessary to reinforce consumer perceptions of the usefulness of and trust in these eHealth technologies while mitigating their anxieties about computer use in general.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".