Training Transfer: Does Training Design Preserve Training Memory?
Bibliographic record
Abstract
Billions of dollars are lost by low application of ineffective training. Fast declination of training memory may contribute this loss. The present study uses theoretical examinations via a conceptual model to examine the relationship between training memory and transfer behaviour. Training design, training retention (training memory), and training transfer are the study variables. The study population, is the federal ministries in the United Arab Emirates (UAE), was assessed via random sampling. Data were collected by a cross-sectional approach via questionnaires. Back-translation (English to Arabic), a pre-test, and a pilot test were applied to ensure that any modifications of the questionnaire items were precise and effective. The study was analysed via PLS-SEM. Based on the results, all of the study’s hypotheses were accepted, and significant relationships were revealed between the study variables. Training design is highly correlated with training retention, i.e., a premium training design will lead to a high preservation of the knowledge and skills gained from the training programme. Due to the low correlation between training retention and training transfer, the training retention was considered a secondary contributor of applying training to the work environment. If mangers and practitioners tend to achieve successful training transfer, their efforts should concentrate on adopting modern training design techniques, which could sufficiently maintain the training memory and increase training transfer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".