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Record W2007438090 · doi:10.1002/hrm.20135

An investigation of training activities and transfer of training in organizations

2006· article· en· W2007438090 on OpenAlexaff
Alan M. Saks, Monica Belcourt

Bibliographic record

VenueHuman Resource Management · 2006
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTraining (meteorology)Transfer of trainingWork (physics)Training and developmentMedical educationTransfer of learningPsychologyKnowledge managementComputer scienceMedicineManagementEngineering

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to investigate the extent to which organizations implement training activities for facilitating the transfer of training before, during, and after training and the relationship between these activities and the transfer of training across organizations.Training professionals from 150 organizations reported that 62%, 44%, and 34% of employees apply training material on the job immediately, six months, and one year after training. In addition, their organizations were significantly more likely to use training activities to facilitate transfer during training than either before or after training. Further, training activities before, during, and after training were significantly related to the transfer of training; however, activities in the work environment before and after training were more strongly related to transfer than activities during training. The practical and research implications of these findings are discussed for improving the transfer of training in organizations. © 2006 Wiley Periodicals, Inc.

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)0.038
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 score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.045
GPT teacher head0.300
Teacher spread0.255 · 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

Citations373
Published2006
Admission routes1
Has abstractyes

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