Using the technology acceptance model to predict patient attitude toward personal health records in regional communities
Bibliographic record
Abstract
Purpose The purpose of this paper is to statistically measure (quantify) how a sample of Canadians perceives the usability of electronic personal health records (PHRs) and, in the process, to increase Canadian patients’ awareness of PHRs and improve physicians’ confidence in their patients’ ability to manage their own health information through PHRs. Design/methodology/approach The authors surveyed 325 Canadian patients living in Northern Ontario to assess a research model consisting of seven perceptions of PHR systems used to manage personal health information electronically, and to assess their perceived ability to use PHR systems. The survey questions were adapted from the 2014 National Physician Survey in Canada. The authors compared the patients’ results with physicians’ own perceptions of their patients’ ability to use PHR systems. Findings First, there was a positive relationship between surveyed patients’ prior experiences, needs, values, and their attitude toward adopting the PHR system. Second, how patients saw a PHR system’s user-friendliness was the strongest predictor of how useful they considered it would be. Finally, of the 243 physician respondents, 90.3 percent believed their patients would not be able to manage their own e-health information via a PHR system, but 54.8 percent of the 325 patient respondents indicated they would be able to do so. Originality/value This study is unique in that the authors know of no other Canadian study that purports to predict, using the technology acceptance model factors, people’s attitudes toward adopting a PHR system. As well, this is the first Canadian study to compare the perspectives of healthcare providers and their patients on e-health applications.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".