The Relationship between Architects’ Quality of Work Life and Their Productivity: A Case Study
Bibliographic record
Abstract
The goal of quality of work life is to evaluate how employees perceive their lives inside a company. Lack of quality of work life is linked to greater levels of occupational stress, anxiety, and burnout, leading to poorer job performance and substantial expenses for organizations. Bearing this in mind, the current study was formulated to address the gap in the literature and examine the relationship between architects’ Quality of Work Life and their productivity throughout a case study in Kerman, eastern Iran. To fulfil the objectives of this study, 130 architects working actively and officially in Kerman agreed to participate in the study, who were reached out using the Krejcie, and Morgan’s (1970) table. Two questionnaires in Persian Version were distributed among them, including Quality of work life questionnaire based on Walton model (1973) and the Productivity questionnaire based on the ACHIEVE model, both adopted from Chalabiei (2017). The data was transferred to SPSS Version 27 for further statistical analyses. The results of the Spearman's correlation coefficient test showed that there was a positive correlation between the quality of work life and the architects’ productivity. Also, all the eight components of quality of work life had a positive relationship with the architects’ productivity. Further implications of the study results and suggestions are discussed, accordingly.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".