A Preliminary Review of the WELL Building Standard
Bibliographic record
Abstract
The natural and built environment can both affect our health and well-being; as nature affects our health, so do buildings – the spaces where we spend most of our time. The WELL Building Standard (or “WELL”) is a voluntary rating system that aims to support, maintain, and promote occupant health and well-being in buildings. The literature indicates increased documentation and evaluation of WELL implementations, but their impact remains unclear. This research aims to study the extent of the effectiveness and impact of WELL implementation in buildings, how it compares to more established rating systems, and directions for future research. A three-step methodology was conducted for this literature review, including (i) an article search, screening, and selection using the Scopus database; (ii) a detailed review of articles evaluating the effectiveness of WELL and comparing WELL to other rating systems; and (iii) a bibliometric analysis of the studies to map and understand how the field has evolved. Preliminary results indicate that WELL-certified buildings generally have higher satisfaction with mental health, well-being, and productivity than non-WELL-certified buildings but do not show improvements in physical health satisfaction. As different subjective satisfaction measures were recorded in each article, a clear conclusion spanning various articles cannot be accurately drawn yet. A meta-analysis of the results of case studies and a more comprehensive and long-term study are needed to determine if WELL upholds its premise and goals and whether it is an effective investment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".