Using the technology acceptance model to predict patient attitude toward personal health records in regional communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 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 it