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Record W99223338

Motivated mentors : an examination of the construct of motivation to mentor, its antecedents and its consequences

2003· dissertation· en· W99223338 on OpenAlexaboutno aff
Melanie Rudnitsky

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2003
Typedissertation
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyConstruct (python library)PersonalityMentorshipSocial psychologyJob satisfactionLocus of controlNegative affectivityApplied psychologyMedical educationMedicine
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.049
GPT teacher head0.325
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2003
Admission routes1
Has abstractyes

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