A mixed-method systematic review of indoor environmental quality (IEQ) gaps and opportunities for future research
Bibliographic record
Abstract
Indoor environmental quality (IEQ) plays a pivotal role in mitigating sick building syndrome and advancing several of the United Nations’ Sustainable Development Goals. Globally recognized as essential for the future of healthy buildings, a comprehensive understanding of IEQ is critical. This study presents a systematic mixed-method review of IEQ research, combining scientometric and qualitative analyses to explore its development, current landscape, and emerging trends. A scientometric analysis of 1200 peer-reviewed articles indexed in Web of Science and Scopus mapped citation patterns and dominant research themes. Findings reveal that while IEQ is a rapidly evolving, multidisciplinary field, most research has focused on occupant health and well-being and energy performance, primarily through the lens of thermal comfort. Emerging areas, such as occupant behaviour, cognitive performance, and cross-domain interactions, are only beginning to gain scholarly attention. Collaborations analysis shows the dominance of the USA and China, with limited contributions from South America and Africa. A qualitative review of 250 selected studies further highlights critical gaps, including small sample sizes, limited exposure-health response data, overdependence on controlled laboratory settings, lack of longitudinal studies, insufficient integration of artificial intelligence for IEQ optimization, and underexplored diversity in health endpoints and study populations. The review also identifies the need to bridge the climate change–IEQ nexus. Together, these findings emphasize the importance of applied, interdisciplinary, and long-term research to better understand and enhance IEQ in real-world contexts. This study offers valuable insights and a forward-looking agenda to guide future research and innovation in the field of IEQ.
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 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 | 0.087 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".