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Record W2331098870 · doi:10.5539/gjhs.v8n10p295

Mediation Effect of Self-Efficacy on the Relationship between Mentoring Function and Career Advancement among Academics in Iran

2016· article· en· W2331098870 on OpenAlexvenueno aff
Bita Parsa, Parisa Parsa, Nakisa Parsa

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
FundersHamadan University of Medical Sciences
KeywordsPromotion (chess)MediationSelf-efficacyPsychologyStructural equation modelingCareer developmentMedical educationMedicineSocial psychologyPolitical scienceSociologySocial scienceMathematics

Abstract

fetched live from OpenAlex

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.078
GPT teacher head0.380
Teacher spread0.303 · 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 designObservational
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

Citations8
Published2016
Admission routes1
Has abstractyes

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