Strategies for Well-being in New Work Spaces: A Case Study in a Post-Pandemic Context
Bibliographic record
Abstract
People's priorities have changed as a result of the COVID-19 pandemic, with impacts on architectural experiences and work spaces in particular as teleworking and technology have become increasingly relevant in this new reality. Moreover, there is increased interest in the impact that spaces have on health, productivity and well-being as variables such as lighting, acoustics, biophilia, shape, composition, size and more influence perception and emotional, cognitive and behavioural states. Evidence-based design makes it possible to scientifically understand information about the situations present before and after an action, providing a more holistic view of the phenomenon through parameterisation and producing an impact on decision-making as seen in the case of this study. This article presents a case study developed through a mixed methodology that combines theoretical research methods to gain scientific knowledge on the topic and trends in the sector, as well as empirical methods to study the specific context of the corporate headquarters at Tous in Manresa. As to the theoretical side of the paper, we have conducted a literature review in the WOS (Web of Science) database, complemented by two trend reports on the future of workspaces. Regarding the empirical study, we programmed three different sources to compile data from workers at different times, spaces and platforms. In parallel, we measured the parameters of the built environment in different locations over two work days. Among the results, the following stand out: the universe of relationships, evidenced by cross-disciplinary departments such as HR (human resources) and IT (computer technology), as well as the relationship between the Product, Sales, R&D and After-Sales departments; the status of employees, with neutral or positive values in cognitive states, and of the environment, space lacking colour and with little brightness and neutral in terms of light colour, atmosphere and ventilation; the detection of the positive aspects to improve and to incorporate; and the measurement of the physical parameters of the environment, high noise level, CO2 within the comfort range, high temperatures and over illuminated or poorly lit spaces, and their perception. Finally, we propose scientific evidence and trends arising from the relationship between objective and subjective data as a result of design strategies focused on people's well-being. These results are taken as the basis for making the changes implemented within a space.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".