Studying the Effective Factors on Quality of Human Sources Training Plans (Case study: Employees of Iran's Saderat Bank- Tehran's West Superintendence)
Bibliographic record
Abstract
<span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">The purpose of this paper is to study the effective factors on quality of human sources training plans. Current research in terms of purpose is applicable and in terms of nature, it is descriptive and correlation kind and in terms of method, it is a survey research. The statistical population includes all employees who work in Saderat Bank of Tehran<sup>'</sup>s west superintendence that the total number was 200 persons. The sample size was selected 131 persons according to Cochran<sup>'</sup>s formula and the sampling method was simple random. In order to collect the data, the researcher-made questionnaire with 37 items in the form of 5-degree Likert spectrum from too high to too low was used. The face and content validity of questionnaire was confirmed by some of the experts and knowledgeable persons. Since Cronbach<sup>'</sup>s Alpha coefficient for the variables of curriculums (0.875), training environment (0.942), work environment (0.759), personality characteristics of trainees (0.901), quality of in-service training plans (0.867) was obtained higher than 0.7, therefore the reliability of the questionnaire is confirmed. In order to analyze the data, one-sample t-test and two-variable linear Regression with spss software were used. The results indicated the variables of curriculums with β coefficient of 0.068 percent, training environment with β coefficient of 0.379 percent, work environment with β coefficient of 0.762 percent and personality characteristics of trainees with β coefficient of 0.241 percent have the power of predicting the dependent variable changes of in-service training plans quality. The adjusted explanation coefficient was 0.980 that indicated 4 independent variables of the research have been able to predict 98 percent of dependent variable changes.</span>
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".