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Record W2119542151 · doi:10.5267/j.msl.2015.2.013

Investigating the effect of in-service training on advisors' effectiveness through psychological empowerment

2015· article· en· W2119542151 on OpenAlexvenueno aff
Farideh Dokaneheeifard, Mahnaz Jafari

Bibliographic record

VenueManagement Science Letters · 2015
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)PsychologyService (business)EmpowermentApplied psychologySocial psychologyKnowledge managementBusinessMarketingComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Efficiency is one of the fundamental concepts in any organization including ministry of education of Iran.Teachers and counselors are the main assets of this organization and education plays a key role in achieving the organization's goals.In-service training is a technique for improving the quality and effectiveness of the advisors.This paper presents a study on the effect of in-service training on advisors' effectiveness through psychological empowerment.The study uses a questionnaire developed by Spreitzer (1995) [Spreitzer, G. M. (1995).Psychological empowerment in the workplace: Dimensions, measurement, and validation.Academy of management Journal, 38(5), 1442-1465.]to examine the effects of five variables; namely self-efficacy, self-determination, impact, meaningfulness and trust.Using structural equation modeling, the study has determined that all five psychological empowerment components had positive and meaningful effects on in-service training.In addition, in-service training maintained positive and meaningful impacts on all components on psychological empowerment.Moreover, in-service training positively influenced on psychological empowerment.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.065
GPT teacher head0.410
Teacher spread0.345 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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