The completion behaviour of registered apprentices in Canada: who continues, who quits, and who completes programs?
Bibliographic record
Abstract
The number of registered apprentices in Canada more than doubled between 1995 and 2007, yet successful completion of apprenticeship programs increased by only about one-third as much. Uncovering the factors related to low completion rates is a necessary first step to ensuring that today’s skilled labour is replaced in the future. This study utilizes a series of multinomial probit models and the 2007 National Apprenticeship Survey (NAS) to investigate the completion behaviour of individuals enrolled in apprenticeship programs. These behaviours include continuing, discontinuing (or quitting), and completing programs. The NAS contains detailed demographic information regarding respondents’ backgrounds and the characteristics of apprenticeship programs. Program completion is positively related to a variety of demographic characteristics, including being married and having completed at least a high school education prior to beginning an apprenticeship. Males and females have similar completion probabilities. Completion is negatively related to time in the apprenticeship program (beyond the normal program length) and the number of employers during training. Type of technical training and having a journeyperson always present enhance the probability of completion. The regional unemployment rate has little effect on whether an individual completes an apprenticeship program or not. There are also large provincial and trade group differences. Although this research has identified a number of factors correlated with apprenticeship completion, further research could address the benefits of completion such as wages and probability of employment. A more detailed examination of the variety of obstacles encountered by apprentices during training may also be useful in redesigning programs to enhance completion.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".