Using discrete choice experiments to value benefits and risks in primary care
Bibliographic record
Abstract
Discrete choice experiments (DCEs) are a stated preference valuation method. As a ubiquitous component of healthcare delivery, risk is increasingly used as an attribute in DCEs. Risk is a complex concept that is open to misinterpretation; potentially undermining the robustness of DCEs as a valuation method. This thesis employed quantitative, qualitative and eye-tracking methods to understand if and how risk communication formats affected individuals’ choices when completing a DCE and the valuations derived. This thesis used a case study focussing on the elicitation of women’s preferences for a national breast screening programme. Breast screening was chosen because of its relevance to primary care and potential contribution to the ongoing debate about the benefits and harms of mammograms. A DCE containing three attributes (probability of detecting a cancer; risk of unnecessary follow-up; and cost of screening) was designed. Women were randomised to one of two risk communication formats: i) percentages only; or ii) icon arrays and percentages (identified from a structured review of risk communication literature in health).Traditional quantitative analysis of the discrete choices made by 1,000 women recruited via an internet panel revealed the risk communication format made no difference in terms of either preferences or the consistency of choices. However, latent class analysis indicated that women’s preferences for breast screening were highly heterogeneous; with some women acquiring large non-health benefits from screening, regardless of the risks, and others expressing complete intolerance for unnecessary follow-ups, regardless of the benefits. The think-aloud method, identified as a potential method from a systematic review of qualitative research alongside DCEs, was used to reveal more about DCE respondents’ decision-making. Nineteen face-to-face cognitive interviews identified that respondents felt more engaged with the task when risk was presented with an additional icon array. Eye-tracking methods were used to understand respondents’ choice making behaviour and attention to attributes. The method was successfully used alongside a DCE and provided valid data. The results of the eye-tracking study found attributes were visually attended to by respondents most of the time. For researchers seeking to use DCEs for eliciting individuals’ preferences for benefit-risk trade-offs, respondents were more receptive to risk communicated via an icon array suggesting this format is preferable. Policy-makers should acknowledge preference heterogeneity, and its drivers, in their appraisal of the benefits of breast screening programmes. Future research is required to test alternative risk communication formats and explore the robustness of eye-tracking and qualitative research methods alongside DCEs.
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 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 | 0.088 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".