Bibliographic record
Abstract
Early trials of Electronic Personal Health Records (ePHRs) show they provide two strong benefits: better healthcare outcomes and lower taxpayer costs. However, consumers are concerned about the possible loss or misuse of personal health data. For people to adopt ePHRs, they must trust both the system and the operating organization. The model presented here studies consumers’ likelihood of adopting ePHRs, combining trust, distrust, risk, motivation, and ease of use; as well as their perceptions of government, software vendors, and physicians as providers of ePHRs. Based on the Technology Acceptance Model, and incorporating elements of trust-distrust dualism and perceived risk, the model was tested empirically using survey data from 366 Canadian adults. The model explains 52 percent of the variance in the intention to use an ePHR, with strong negative effects from perceived risk and distrust, and strong positive effects from trust and perceived usefulness. Other findings include further evidence that trust and distrust are different constructs, not ends of a spectrum; that Canadians’ relationship with their healthcare system is complex; and that the risks in using an online system can be overcome by the perceived benefits. Open-ended responses show that people generally trust their doctors, but are sceptical that a doctor could provide a secure ePHR. Responses indicated that participants liked the consolidation of data and ease of access, but feared loss of privacy.
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.
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 | 0.011 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".