Consumer acceptance of biometrics for identity verification in financial transactions
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.
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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 | 0.006 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".