Mediation Effect of Self-Efficacy on the Relationship between Mentoring Function and Career Advancement among Academics in Iran
Bibliographic record
Abstract
BACKGROUND: Despite the importance of social organizational factors in career advancement and promotion among academic employees, still some academic employees suffer from low career advancement and consequently low academic performance. Aim of this study was to examine the mediation effect of self-efficacy on relationship between mentoring and career advancement among academic employees in the two public universities in Iran. METHODS: This survey research was done among 307 randomly selected academic employees to determine predictors of their career advancement. Self-administered questionnaires were used to collect data. The Structural Equation Modelling (SEM) methodology was applied to determine the best fitted model to predict career advancement. Analysis of data was performed using the Pearson's correlation analysis and SEM. RESULTS: The results show that self-efficacy was related to mentoring and career advancement (p<0.05). The effect of mentoring on career advancement was significant (p<0.05). Self-efficacy partially mediated the relationship between mentoring and career advancement (p<0.05). CONCLUSION: Academics need to be equipped with appropriate skills such as mentoring and enhance their self-efficacy to improve academic career advancement.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".