Bibliographic record
Abstract
Purpose This paper aims to examine the personal and organisational factors that affected public sector managers' participation in leadership training programmes and their ability to transfer learning to their workplace. Design/methodology/approach In‐depth interviews were conducted with five Canadian and five Northern Irish managers who participated in one‐day leadership training programmes. Findings The uncertain environment throughout the public sector was the greatest inhibitor to training participation and transfer. However, other training characteristics and training design features were also noted (e.g. motivation, trainer influence). Practical implications Public sector organisations must take concrete steps to address current environmental challenges to fully benefit from leadership training programmes. The paper highlights pre‐, during, and post‐training strategies that can be implemented. Originality/value The findings illustrate that leaders in both public sector jurisdictions face similar issues and these have been exacerbated by the current turbulent climate. The authors suggest that to maximise return on training investment the public sector must create an environment supportive of training participation and transfer and suggest recommendations to help organisations in the future. These findings were facilitated by the use of qualitative training evaluation methods, not traditionally used in training transfer research.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.097 | 0.024 |
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".