Editorial: Design quality: what we learned from evidence-based design and post-occupancy evaluation research during the COVID-19 pandemic
Bibliographic record
Abstract
evidence based design, post-occupancy evaluation (POE), COVID-19 pandemic, design quality, healthy environments Editorial on the Research Topic Design quality: what we learned from evidence-based design and postoccupancy evaluation research during the COVID-19 pandemicThe COVID-19 pandemic has resulted in 6.8 million deaths worldwide, and millions more have been infected, suffered symptomatic illnesses or hospitalizations (WHO, 2023).The pandemic required, and still requires, massive societal and organizational shifts to prevent or reduce the further spreading of the virus.Lockdowns and mandatory remote work and study at home greatly impacted people's daily lives."Prior to the COVID-19 crisis, most workers had limited familiarity with remote working" (Battisti et al., 2022, p.1).According to (Wang et al., 2021), before COVID-19, only 2.9% of the total US workforce and around 2% of that in Europe engaged in emergency remote working.As a result, "the pandemic abruptly upset normal work routines and accelerated previously ongoing trends relating to the migration of work to online or virtual environments (Kniffin et al., 2021; Battisti et al., 2022, p. 1).Remote working and online education are not new, but previously were mainly done voluntarily.Due to COVID-19, the development and adoption of digital and information and communication technologies (ICTs) have increased dramatically.As such, the pandemic can be perceived as a giant real-life human experiment, from which many lessons can be learned about the impact of a pandemic on people's quality of life, wellbeing, performance, sense of belonging to a particular community or organization, and social cohesion.Organizations and governments now ask themselves what measures are needed in a post-pandemic period and how to cope with future pandemics.For Frontiers in Built Environment, a particular question is what policymakers, designers, corporate real estate and facility managers can or should do to design and manage a built environment that supports people's wellbeing, performance and quality of life during a pandemic and in a post-pandemic context.Relevant challenges for practitioners and related research questions are:
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 | 0.022 | 0.111 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.023 | 0.022 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".