Using a Discrete Choice Conjoint Experiment to Engage Stakeholders in the Design of an Outpatient Children’s Health Center
Bibliographic record
Abstract
OBJECTIVES: To engage users in the design of a regional child and youth health center. BACKGROUND: The perspective of users should be an integral component of a patient-centered, evidence-based approach to the design of health facilities. METHODS: We conducted a discrete choice conjoint experiment (DCE), a method from marketing research and health economics, as a component of a strategy to engage users in the preconstruction planning process. A sample of 467 participants (290 staff and 177 clients or community stakeholders) completed the DCE. RESULTS: Latent class analysis identified three segments with different design preferences. A group we termed an enhanced design (57%) segment preferred a fully featured facility with personal contacts at the start of visits (in-person check-in, personal waiting room notification, volunteer-assisted wayfinding, and visible security), a family resource center with a health librarian, and an outdoor playground equipped with covered heated pathways. The self-guided design segment (11%), in contrast, preferred a design allowing a more independent use of the facility (e.g., self-check-in at computer kiosks, color-coded wayfinding, and a self-guided family resource center). Designs affording privacy and personal contact with staff were important to the private design segment (32%). The theme and decor of the building was less important than interactive features and personal contacts. CONCLUSION: A DCE allowed us to engage users in the planning process by estimating the value of individual design elements, identifying segments with differing views, informing decisions regarding design trade-offs, and simulating user response to design options.
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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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".