Understanding Decision-Making in the Adoption of Digital Health Technology: The Role of Behavioral Economics’ Prospect Theory (Preprint)
Bibliographic record
Abstract
<sec> <title>UNSTRUCTURED</title> The decision to accept or reject new digital health technologies remains an ongoing challenge among health care patients, providers, technology companies, and policymakers. Over the past few decades, interest in understanding the choice to adopt technology has led to the development of numerous theories and models. In 1979, however, psychologists Kahneman and Tversky published their seminal research article that has pioneered the field of behavioral economics. They named their model the prospect theory and used it to explain decision-making behaviors under conditions of risk and uncertainty as well as to provide an understanding of why individuals may make irrational or inconsistent choices. Although the prospect theory has been used to explain decision-making in economics, law, political science, and clinically, at the individual level, its application to understanding choice in the adoption of digital health technology has not been explored. Herein, we discuss how the main components of the prospect theory’s editing phase (framing effect) and evaluation phase (value function and weighting function) can provide valuable insight on why health care patients, providers, technology companies, and policymakers may decide to accept or reject digital health technologies. </sec>
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How this classification was reachedexpand
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".