Consumer acceptance of biometrics for identity verification in financial transactions
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
Recently, there has been a growing trend towards consumer-based healthcare in which consumers are increasingly becoming partners in their own care. One way of accomplishing this is to provide consumers with access to their health records through the use of Personal Health Record (PHR) systems. In spite of their potential benefits, recent research has shown that PHRs are not yet popular or well known to consumers. The overall objective of this research is to investigate the influences of various personal, behavioral, and environmental factors on the adoption and use of PHR systems by Canadian consumers. Drawing on both the information systems and behavioral healthcare literatures such a model is developed and presented. The proposed model will be validated using a longitudinal design over a period of 16 months involving patients from two local clinics. The study participants will be introduced to an existing PHR system at those clinics. The system will subsequently be made available for their potential use. Users will be surveyed at various points in time regarding their perceptions about the system utilizing both close-ended and open-ended questions. Collected data will be analyzed using structure equation modeling and qualitative data analysis techniques.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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