PERCEIVED EFFECTIVENESS AND ALIGNMENT OF TRAINING IN CANADIAN EDUCATIONAL INSTITUTIONS
Bibliographic record
Abstract
This research examines how staff members at educational institutions in Canada perceive the effectiveness of training, whether the types of training they deem necessary align with those they participate in, and whether their perspectives change based on years of experience, educational attainment, and institution type. A cross-sectional online survey was conducted involving 50 employees. The effectiveness was evaluated using a five-point scale, with two items in multiple-choice format (types of training deemed necessary and attended). Within-subject comparisons, one-way analysis of variance for comparing multiple groups, and analysis of differences between two independent groups assuming unequal variances were utilized. The findings indicate a significant gap: participants identified more “necessary” training categories than they attended; no differences were observed based on experience; differences in education favour those with higher education levels (with higher and more consistent ratings); private institutions exhibit a more positive distribution of grades compared to public ones, although the difference in mean scores is not statistically significant in this sample. It is concluded that aligning training offerings with expressed needs and acknowledging the trainees' profiles is more crucial than the training experience itself. The study suggested conducting an annual needs assessment that aligns with strategic goals, designing training focused on practical application (through scenarios, practice, mentoring, and implementation strategies), engaging managers before and after training, customizing based on educational segments, and systematically evaluating outcomes with follow-up post-training. Furthermore, responses were anonymous, items were mandatory, and the scale ranged from 1 to 5; caution is advised when interpreting results from the small group with doctoral degrees.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".