A social work study using factor analysis on detecting important factors creating stress: A case study of hydro-power employees
Bibliographic record
Abstract
The study performs an empirical study based on the implementation of factor analysis to detect different factors influencing people to have more stress in a hydropower unit located in city of Esfahan, Iran. The study performed the survey among all 81 people who were working for customer service section of this company and consisted of two parts, in the first part; we gather all private information such as age, gender, education, job experience, etc. through seven important questions. In the second part of the survey, there were 66 questions, which included all the relevant factors impacting employees' stress. Cronbach alpha was calculated as 0.946, which is well above the minimum acceptable level. The implementation of factor analysis has detected 16 important groups of factors and each factor is determined by an appropriate name. The results of our factor analysis show that among different factors, difficulty of working condition as well as work pressure are two most important factors increasing stress among employees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".