A consumer adoption model for personalized medicine: an exploratory study
Bibliographic record
Abstract
Purpose – The purpose of this research is to identify factors linked to the potential acceptance of personalized medicine (PM) by consumers. Roger’s diffusion of innovation model (1995) and the work of Duguay et al. (2003) on transgenic biopharmaceuticals contributed to the development of the proposed conceptual model. Design/methodology/approach – The study design was an exploratory cross-sectional survey that used a Canadian national online panel of 307 respondents. Findings – The results suggest that the most important factors leading to consumer adoption of PM are knowledge, relative advantage and compatibility with existing values. The level of homophilus traits was negatively related to the acceptance of PM. Originality/value – Marketers will need to provide documented evidence of PM’s benefits over existing therapy based on improved efficacy and reduced side effects. Further, concerns about higher price, product distribution and drug reimbursement policies may limit its acceptance. This is the first study to examine the potential adoption and acceptance of PM by consumers.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".