Factors leading to acceptance of and willingness to pay for predictive testing among chronically Ill patients
Bibliographic record
Abstract
Background: Personalized medicine can lower healthcare system costs and help ensure that chronic patients get the most appropriate treatment for their individual illness. However, individual factors leading to acceptance of personalized medicine technologies such as predictive testing by chronic patients remain largely unknown. Objectives: This study was aimed at identifying individual sociodemographic factors leading to the acceptance of predictive testing in chronic patients and their willingness to pay for these tests. Design and methods: A web survey was conducted with 210 Canadian patients affected by Inflammatory Bowel Disease (IBD). The data was processed using the SPSS software. Inferential statistical analyses were conducted using the chi square test. Results: Chronic patients were massively in favour of undergoing a genetic test that could predict response to treatment options, and the majority were in favour of paying for the test. While the population in general has concerns regarding genetic testing, the present study indicates that the seriousness of chronic illness along with side effects of treatments leads to a higher acceptance among patients. Only the yearly number of consultations was positively related to acceptance and willingness to pay. Conclusions: While previous studies have reported resistance to genomic technologies amongst the general population, the present study demonstrates that chronic patients are largely open to genetic predictive testing. Furthermore, a vast majority was even prepared to pay for such tests. Clinicians, healthcare organizations and pharmaceutical companies should take this result into consideration when building and promoting predictive testing options. Based on these findings, future studies are warranted for further investigating and characterizing patients’ perception towards genetic predictive testing across other chronic illnesses.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".