Trainee Perceptions of Structured On-the-Job Training (OJT): The Impact of Trainer Experience and Use of Structured OJT Guides
Bibliographic record
Abstract
This research study investigated the influence of trainer experience on the trainer’s use of directive behaviors and communication clarity in structured on-the-job training (OJT) and how those variables, in turn, impacted trainee perceptions. It was predicted that trainer experience would be positively related to trainer characteristics (directive behaviors and communication clarity), and that the use of structured OJT guides would have a conditional impact on this relationship. It was also predicted that trainer characteristics would be positively related to trainee perceptions. Finally, it was predicted that the conditional relationship of trainer experience and use of structured OJT guides would influence trainee perceptions via trainer characteristics. To test the hypotheses, data were collected from 76 current and recent technical trainees and trainers at electrical utilities across the United States and Canada. Trainer experience was found to not have a significant relationship with either directive behaviors or communication clarity. The use of structured OJT guides did, however, emerge as having a significant independent influence on trainer characteristics. Lastly, trainer characteristics (directive behaviors and communication clarity) were positive predictors of perceptions of the trainer and the overall training effectiveness. This study demonstrated that while experience may not be the most important factor for an OJT trainer to possess in terms of influencing the training session and perceptions of the trainee, the use of structured OJT guides is important for creating a productive and effective training environment.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".