Training Needs Assessment of Technical Skills in Managers of Tehran Electricity Distribution Company
Bibliographic record
Abstract
<p class="apa">Current dissertation has been conducted in order to investigate and detect training needs of the mangers (top and middle) in Tehran Electricity Distribution Company. Research method is applied kind based on its purpose. Due to data collection method, this query is descriptive-survey type. Statistical population in this study is all of managers in Tehran Electricity Distribution Company in 2014 who are 144 men. Sample size has been determined 108 persons referring to the Morgan’s table. To sample, multi-steps clustering method has been applied. Data collected using questionnaires. Questionnaire’s validity has been obtained using comments by experts, guidance professor and consultant professors and its reliability was obtained via experimental implementation and calculating Cronbach’s alpha which is equal to 0.93 Collected data were analyzed using descriptive statistical techniques (Mean, median, mode, standard deviation, skewness, elongation, minimum and maximum) and inferential statistical techniques (single group Chi-square test, independent t-test and Friedman’s One-way Analysis of Variance and post hoc LSD test). Research findings imply that training needs assessment of technical skills in directors are: Technical issues, how to use computer and internet, Personnel and administrative matters, administrative rules and regulations, administrative correspondence principles and archive mechanisms, staff evaluation, appropriate use of funds, supervision, respectively. Also, it was manifested that there is a significant difference between training needs assessment of directors’ technical skills based on their experience. No significant difference was observed between managers’ technical skills based on their educational degree.</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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".