Overcoming the Challenges Inherent in Conducting Design Research in Mental Health Settings
Bibliographic record
Abstract
OBJECTIVE: Conducting high-quality design research in a mental health setting presents significant challenges, limiting the availability of high-quality evidence to support design decisions for built environments. Here, we outline key approaches to overcoming these challenges. BACKGROUND: In conducting a rigorous post-occupancy evaluation of a newly built mental health and addictions facility, St. Joseph's Healthcare, Hamilton, we identified a number of systematic barriers associated with conducting design research in mental health settings. METHODS: Our approach to overcoming these barriers relied heavily upon (i) selecting established measures and methods with demonstrated efficacy in a mental health context, (ii) navigating institutional protocols designed to protect vulnerable members of this population, and (iii) designing innovative data collection strategies to increase participation in research by individuals with mental illness. Each of these approaches drew heavily on the expert knowledge of mental health settings and the experiences with mental health, facilities management, and research of a research team that was well integrated within the parent institution. CONCLUSIONS: Engaging multiple stakeholders (e.g., care providers, patients, ethics board, and hospital administrators) contributed their trust and support of the research. Traditionally, post-occupancy evaluation researchers are independent of the facilities they research, yet this is not an effective approach in mental health settings. We found that, in working toward solutions to the three obstacles we described, having team members who were well "networked" within the parent institution was necessary. This approach can turn "gatekeepers" into champions for patients' engagement in the research, which is essential in generating high-quality evidence.
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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 | 0.757 | 0.759 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.012 | 0.037 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.012 | 0.024 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".