A new reality: supporting employee well-being within a hybrid workplace model
Bibliographic record
Abstract
Work, working, and the workplace has been the foundation of urbanized civilization for thousands of years. Yet, it was only in the early twenty-first century that commercial interior designers had truly become concerned with how these interiors affected the well-being of those who worked within (Beno, 2021). While great strides have been made, the advent of the global COVID-19 pandemic in March 2020 reinvigorated the focus on health and wellness in commercial design, especially as employees have begun returning to the office. As such, the office and its subsequent design have entered the next phase of its evolution, a hybrid workplace model (Hogarty, 2021b) that aims to increase employee well-being through enhanced flexibility, digital collaboration methods, and interior environmental quality. This practicum explores theories and ideas about enhancing employee well-being through concepts in environmental psychology, supportive design theory, universal design, and the latest commercial interior planning strategies resulting or emerging from the COVID-19 pandemic. The final hypothetical design solution was derived from a culmination of information and approaches generated from a literature and precedent review, comprehensive site analysis, spatial programming, and design concept development. The site selected for the hypothetical design is 136 Market Avenue in Winnipeg, Manitoba’s iconic downtown Exchange District.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".