Demonstrating the Effect of the Built Environment on Staff Health-Related Quality of Life in Ambulatory Care Environments
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of the built environment on staff health-related quality of life (HRQoL) in a federally qualified health center (FQHC). BACKGROUND: Staff within FQHCs face many challenges that can significantly impact their HRQoL. Design features directed toward reducing stress have been shown to improve staff health outcomes in acute care environments. However, minimal literature exists linking design features to health outcomes within FQHCs. METHOD: A cross-sectional, observational study was conducted involving three FQHCs that contain varying levels of enhancements to their interior features. A total of 75 staff across the sites participated in the indoor environmental quality (IEQ) survey, measuring satisfaction and perceived productivity. Measurements for staff HRQoL were captured using the quality of well-being (QWB) scale, which was administered to 10 staff at each site. Standard regression diagnostics were used to examine fit and find influential observations. RESULTS: QWB scores were normally distributed, and a dose-response relationship was found between QWB scores and level of enhancements. As the categories of satisfaction and perceived productivity increased, the average QWB score increased. Regression models showed overall statistical significance and predicted between a quarter to a half of the change in QWB scores. CONCLUSIONS: This pilot study suggests that the more enhancements included in the interior features of a FQHC, the greater the returns to staff HRQoL. Findings also suggest that staff with a lower QWB appreciate enhancements more. Design strategies associated with improved staff well-being should be evaluated in terms of the amount of HRQoL they contribute.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".