The Role of Design Characteristics in Enhancing Sense of Coherence in Workplace Environments: A Case Study of University of Mosul Buildings
Bibliographic record
Abstract
The study of design characteristics in built environments is a critical aspect of promoting human health, as individuals spend a considerable portion of their lives in these settings.As built environments significantly influence individual and societal well-being, it is imperative to prioritize health-supportive features.Integrating a salutogenic approach into the design process is essential for fostering healthy communities.The health-promoting design emphasizes the origins of health, exploring factors that enhance health rather than merely addressing disease treatment or prevention.In contrast to the traditional disease-focused approach, this study emphasizes factors that support health promotion and encourage individuals or societies to develop a heightened sense of health, well-being, and improved quality of life amidst rapid urbanization.Environmental factors contributing to stress, tension, and various health issues can lead to detrimental changes in individuals' lives.This research investigates the impact of workplace environments on overall health and, specifically, workers' mental health by evaluating the influence of design characteristics on the Sense of Coherence (SOC).By examining the structural composition of spaces that facilitate social interaction and gatherings, this study aims to understand how these spaces can enhance psychological well-being through increased social engagement.The findings contribute to the development of evidence-based design strategies for creating health-promoting built environments in workplace settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".