Analyzing the Relationship between Personnel's Education and Psychological Competence on Quality of Service: The Mediation Role of Organization Commitment in Ministry of the Interior
Bibliographic record
Abstract
<p>The goal of the present paper is to analyze the effect of personnel's education and psychological competence on<br />quality of service. The mediation role of organizational competence in Ministry of the Interior is of<br />descriptive-correlational method. To do that, the standard questionnaire psychological competence by Spriters<br />(1995), personnel education and quality of service by Deher (2015) and organizational commitment by Alen and<br />Mier (1990) were used. The statistical population of the research includes all personnel of Ministry of the<br />Interior which are 1600 subjects. Based on Cochran's formula, 3100 subjects were selected randomly. In order to<br />analyze data the Pearson's correlation test and structural equation of data analysis were used by SPSS and AMOS<br />software. The findings of the research indicate that personnel's education has a positive effect on organizational<br />competence and quality of service (with Alpha level of 0.05). Moreover, the psychological competence is<br />positively affect the quality of service (with Alpha level of 0.05) and organizational commitment affect the<br />quality of service. Finally, it was revealed that the personnel training through organizational commitment affect<br />the quality of service. But, psychological competence does not affect the quality of service through<br />organizational commitment. Moreover, psychological competence does not affect the organizational commitment.<br />The significance levelof the model turned out to be more than the first type error (0.05). This shows that the<br />significant adaption of the estimated model with the present research model. Furthermore, the AGFI and GFI<br />indicators are more than the estimated value (0.9). These indicators show that the model has a capability in<br />estimating the ratio of each factor.</p>
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".