Evaluating Intention and Effect
Bibliographic record
Abstract
OBJECTIVE: This post occupancy evaluation (POE) assessed the impact of architectural design on psychosocial well-being among patients and staff in the context of a new complex continuing care and rehabilitation facility. BACKGROUND: Departing from typical POEs, the hospital design intentions formed the theoretical basis to assess outcomes. Intentions included creating an environment of wellness; enhancing connection to the community, the city, and nature; enhancing opportunities for social interaction; and inspiring activity. METHODS: A pretest-posttest quasi experiment, including quantitative surveys, assessed the impact of the building design on well-being outcomes across three facilities-the new hospital, the former hospital, and a comparison facility with a similar population. RESULTS: With the exception of connection to neighborhood (for patients) and opportunities to visit with others (for staff) and wayfinding (for patients and staff), impressions of the new hospital mirrored the design intentions relative to the former hospital and the comparison facility among patients and staff. Perceptions of improvement in mental health, self-efficacy in mobility, satisfaction, and interprofessional interactions were enhanced at the new hospital relative to the former hospital, whereas optimism, depressive symptoms, general well-being, burnout, and intention to quit did not vary. Interestingly, patients and staff with favorable impressions of the building design fared better on most well-being-related outcomes relative to those with less favorable impressions. CONCLUSIONS: Beyond the value of assessing the impact of the design intentions on outcomes, the approach used in this study would benefit evaluation strategies across a diversity of health and other public and large-scale buildings.
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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 | 0.109 | 0.250 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 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; 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".