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Record W2009620157 · doi:10.1108/02610150610645986

Benefits of mentoring to Australian early career women managers and professionals

2006· article· en· W2009620157 on OpenAlexaff
Ronald J. Burke, Zena Burgess, Barry John Fallon

Bibliographic record

VenueEqual Opportunities International · 2006
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsYork University
Fundersnot available
KeywordsOriginalityPsychologyPsychosocialCareer developmentJob satisfactionSocial psychology

Abstract

fetched live from OpenAlex

Purpose This study aims to examine potential benefits from a mentor relationship to women managers and professionals in early career. Design/methodology/approach Data were collected from 98 women business school graduates using an anonymously completed questionnaire. Respondents identified a more senior individual who had a positive influence in the development of their career, provided descriptive characteristics of this relationship and described its character. Three mentor functions were considered: role model, career development and psychosocial. Findings There were few differences as a function of the gender of the mentor though respondents having female mentors indicated more role modeling and tended to report more psychosocial functions. Respondents reporting more mentor functions also indicated higher levels of job and career satisfaction, more optimistic future career prospects and fewer psychosomatic symptoms. Originality/value Adds to the understanding of mentoring by including psychological well‐being variables as potential mentoring outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.114
GPT teacher head0.344
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations47
Published2006
Admission routes1
Has abstractyes

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