A Survey of the Relationship between the Psychological Capital Components and Staff’ Productivity: A Case of the Genaveh County Offices of Education
Bibliographic record
Abstract
The main purpose of this paper was to investigate the relationship between the psychological capital components and employees productivity of the Genaveh County offices of education. This is an applied and descriptive-survey research. The research population is the 1324 staff of the offices of education of Genaveh County in 2015. The sample includes 297 people selected using Morgan’s table and the stratified random sampling method. The data were collected using Luthans’ psychological capital questionnaire (2007) and Hersey & Goldsmith human resources productivity questionnaire (1984). To analyze the data, Pearson correlation coefficient and multiple regression were used simultaneously. All this was done using SPSS Software Version 21. Results revealed that there is a positive and significant relationship between the psychological capital components and staff productivity at the level of (P<0.001). Results of the regression analysis also indicated that psychological capital components have a significant effect on productivity (F292, 4) =14.1, P<0.001). Also, the R2 value showed that psychological capital can explain 15% of the variance in productivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".