The public are receptive to risk-based innovations: a multi-methods exploration of anticipated acceptability and uptake of novel technologies for cancer early detection in symptomatic and asymptomatic scenarios
Bibliographic record
Abstract
Introduction New technologies and innovations are emerging that enable stratification of individuals based on their risk of cancer and enable screening or diagnostic investigations to be targeted to those at greatest need. This study aimed to explore, in depth, attitudes of the UK public toward this concept; specifically, anticipated acceptability and uptake, including barriers and enablers toward uptake. Methods A survey was completed independently by a representative population sample and alongside a researcher in think aloud interviews. Participants considered three of six exemplars of innovations that enable risk assessment: polygenic risk scores, geodemographic segmentation, continuous biomarker monitoring, minimally invasive tests, artificial intelligence analysis of medical records, and wearable devices. Questions about likelihood of taking up the risk assessment, acceptability of risk-stratified healthcare, and comfort about risk results being used within healthcare generally were set in asymptomatic then symptomatic scenarios. Descriptive statistics and multivariable logistic regression were used to explore differences between the exemplars and contexts and the impact of individual characteristics. Interviews were analyzed using codebook thematic analysis guided by the Theoretical Framework of Acceptability. Free-text comments were also analyzed thematically. Results 999 participants completed the survey independently and 21 participants completed interviews. Most were extremely or somewhat likely to take up risk assessments, ranging from 62.0% for geodemographic segmentation to 85.2% for minimally invasive tests in the asymptomatic scenario, and from 64.2% for geodemographic segmentation to 94.0% for minimally invasive tests in the symptomatic scenario. Acceptability of using the exemplars within risk-stratified screening or referral pathways followed a similar pattern, as did comfort with the results being used widely. Qualitative analyses showed that the innovations and risk-based approach were viewed as proactive and logical. Tests requiring low burden were preferred, although most participants did not consider the burden of any of the innovations to be too high, particularly in the symptomatic context. Conclusions Risk-based innovations for cancer early detection are intuitive. Study participants would be likely to engage and support their use for risk stratification, particularly for decisions about symptom investigations. These findings justify and promote ongoing research to develop these technologies and highlight features that increase public acceptability.
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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 | 0.041 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".