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Record W3165670127 · doi:10.58885/ijbe.v06i1.074.mh

OFF-THE-JOB TRAINING EFFECTIVENESS: THE DIFFERENCE BETWEEN EXPECTATIONS AND EXPERIENCES OF TRAINEES

2021· article· en· W3165670127 on OpenAlexaff
Mariam Magdy Hussein, Tarek A. El Badawy

Bibliographic record

VenueInternational Journal of Business & Economics (IJBE) · 2021
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTraining (meteorology)PsychologyJob trainingOn-the-job trainingMedical educationSignificant differenceApplied psychologyMedicineStatisticsPedagogyMathematicsGeographyVocational education

Abstract

fetched live from OpenAlex

The purpose of this study was to comprehend the effectiveness of an training intervention. Trainees’ direct reactions to training provide organizations with enough feedback concerning their current training and development efforts. The research hypotheses focused on the efficacy gap between the expectations of trainees before training and their experiences after it was conducted. A purposive sample of 45 participants attending a selling skills training course completed a survey adapted from Chimote (2010). The repeated measures test indicated that experiences did not fulfill expectations. The Chi-square test revealed that age and education did not significantly affect the responses of participants. The results are discussed in addition to future research directions and practical implications.

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)0.019
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.062
GPT teacher head0.328
Teacher spread0.265 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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