Motivated mentors : an examination of the construct of motivation to mentor, its antecedents and its consequences
Bibliographic record
Abstract
Increasingly, organizations are becoming aware of the value that is associated with employee mentorship programs, where senior individuals with advanced knowledge and experience (mentors) provide support for, and assist the career progression of junior employees (protégés). Mentors can help new employees adjust to their new organization by providing them the guidance and support they need. They can then continue to act as a mentor by helping their protégés grow and develop within the organization. The present study is one that examined the construct of motivation to mentor, its antecedents and its consequences. Surveys were mailed to MBA Alumni of a large Canadian university, calling for those with mentoring experience to take part. Using theories of motivation, motivation to mentor was examined. Individual personality characteristics of altruism, positive affectivity and locus of control are proposed as independent variables affecting one's motivation to mentor. Job satisfaction was the outcome of motivation that was explored in this study. Findings indicate that the individual personality characteristics discussed are significantly related to motivation to mentor and that job satisfaction with pay and promotion opportunities as well as satisfaction with the nature of the work are two salient outcomes reported by mentors. Theoretical and practical implications, limitations, and future directions are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".