How long should a training program be? A field study of “rules‐of‐thumb”
Bibliographic record
Abstract
Purpose This study aims to examine the question of how long a behavioral skills training program should be in order to result in measurable behavioral change. Design/methodology/approach An empirical field study was conducted to compare two different lengths of time for a managerial skills training program aimed at achieving behavioral change. The training time for the first training condition was based on “rules‐of‐thumb” found in the literature. The training time was increased in an “extended” training condition that covered the same material but permitted more time for lecture, role‐playing and discussion. Findings Results showed that, relative to a control group, participants in the “extended” training condition exhibited behavioral change, but those in the “rules‐of‐thumb” training condition did not. Self‐efficacy increased significantly for trainees in both training conditions. Practical implications More attention is required to the length of training programs as they are being designed, especially if behavioral change is a goal of the training. Using rules‐of‐thumb regarding training length may be insufficient for bringing about behavioral change. More importantly, the need for more effective management skills will not be met, and organizational performance outcomes may be jeopardized. Originality/value The results of this research have the potential to be broadly applicable to management training and may possibly generalize to training in other disciplines where the training is intended to effect behavioral change.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".