Engaging Employers in Apprentice Training: Focus Group Insights from Small-to-Medium-Sized Employers in Ontario, Canada
Bibliographic record
Abstract
Several factors have been identified to influence the registration and retention of apprentices in the construction trades. Employer engagement is a key factor to promote growth in apprenticeships in the construction trades as participation rates continue to be low among small-to-medium-sized employers. In this study, we evaluated the effectiveness of the Ontario Electrical League's (OEL) employer mentorship program through the perspectives of small-to-medium-sized employers using a qualitative approach. Two focus groups were conducted virtually with 11 employers. Focus group audio transcripts were recorded and transcribed for thematic analysis. Themes were generated using a data-driven approach to examine the relationships between mentorship program outcomes and perspectives on industry-related recruitment and retention barriers. Three themes were identified: (a) long-term apprentice recruitment and retention challenges; (b) equity and mental health in the workplace; and (c) industry challenges and mentorship program outcomes. Generally, this sample of employers appreciated the value of the OEL mentorship program through praise of the continued educational support, employer management expertise, hiring resources, and apprentice onboarding tools despite industry barriers in trade stigma, equity and mental health in the workplace, and recruitment and retention challenges. Industry partners should work with these small-to-medium-sized employers to develop workplace initiatives and engage external partners to provide ongoing apprenticeship mentorship support to address the recruitment and retention barriers identified in this study.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".