Ocena przydatności szkoleń i transferu ich efektów na przykładzie banków
Bibliographic record
Abstract
Objective: To examine to what extent knowledge and skills acquired during workplace training (the level of training transfer) are used and the factors differentiating the level of training transfer at banks in Poland.Research Design & Methods: Classification trees were used to analyse data from 1,793 surveys of bank employees obtained as part of wider research.Findings: A high level of training transfer was found overall, though the level was higher in cooperative banks than in commercial ones. Among the remaining factors differentiating the level of transfer, the most important were: the department of work (front / back office), gender, seniority in banking and education type of education. Strong relationship was also found between the level of training transfer and the general assessment of its usefulness.Implications / Recommendations: Training at banks is widely available and highly effective, but a quarter of training participants do not change anything in their work when using it. Some employee groups stand out for effectively transferring their training results. Employees that are highly motivated to improve their competencies have a high level of transfer (women with a shorter period of service, employees without an education in economics / finance, and those employed in operating units).Contribution: The paper demonstrates that using the level of transfer measurement as a competitive method versus the Kirpatrick model is effective.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".