The impact of psychological distance on preferences for prenatal screening and diagnosis for chromosomal abnormalities: A hierarchical Bayes analysis of a discrete choice experiment
Bibliographic record
Abstract
INTRODUCTION: Hypothetical bias continues to be a primary challenge for stated preference methods. The source of hypothetical bias might be approached from the conceptual framework of "psychological distance." By comparing the two samples of pregnant and non-pregnant women, this study aimed to investigate the impact of psychological distance from real-life choice on prenatal screening and diagnostic strategies preferences. METHOD: A discrete choice experiment was conducted among a sample of pregnant women and a sample of the general Canadian population. The attributes included: timing of the results, false-negative rate, false-positive rate, risk of miscarriage, and out-of-pocket cost. The dual response design, including forced and unforced choices, was used. Hierarchical Bayes modelling was employed to estimate part-worth utilities at the individual level. The relative importance scores of the attributes and willingness to pay for improvement in attributes were compared between pregnant and non-pregnant women. Using the individual-level preference weights, we also estimated the uptake rates for various scenarios and compared the two samples. We quantified hypothetical bias by comparing the real-world decision and predicted choices for different strategies for the pregnant and non-pregnant women samples. RESULTS: A sample of 426 pregnant women was matched to 426 non-pregnant women from the general public sample. For pregnant women, the ability to detect chromosomal abnormalities was the most important attribute. For the matched sample of non-pregnant women, false-negative rate and risk of miscarriage were the most important attributes. In addition, pregnant women were willing to pay more for improvement in test characteristics and less sensitive to changes in strategy cost than non-pregnant women. The findings also showed a more significant difference between the actual and predicted choice among non-pregnant women. CONCLUSION: Our findings showed that although both groups valued safer and more accurate screening strategies, there was a difference in willingness to pay, sensitivity to cost, and predictive power of discrete choice experiment estimates between pregnant and non-pregnant women. This difference can be explained by their psychological distance from the decision. In conclusion, psychological distance impacts decision-making and can be identified as a source of hypothetical bias in measuring prenatal screening and diagnosis preferences.
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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 | 0.108 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".