The Apprentice Retention Program: Evaluation and Implications for Ontario
Bibliographic record
Abstract
Attraction and retention of apprentices and completion of apprenticeships are issues of concern to all stakeholders involved in training, economic development and workforce planning. The Canadian Apprenticeship Forum (CAF) has forecast that by 2017 there will be a need to train 316,000 workers to replace the retiring workforce in the construction industry alone (CAF, 2011a). In the automotive sector, shortages are expected to reach between 43,700 and 77,150 by 2021. However, shortages are already widespread across the sector, and CAF survey data show that almost half (48.1%) of employers reported that there was a limited number of qualified staff in 2011 (CAF, 2011a). Given this, retention of qualified individuals in apprenticeship training and supporting them through to completion is a serious issue. There is some indication that registration in apprenticeship programs has been increasing steadily over the past few years, but the number of apprentices completing their program has not kept pace (Kallio, 2013; Laporte & Mueller, 2011). Increasing the number of completions would result in a net benefit to both apprentices and employers, minimizing joblessness and skills shortages. Apprentices face many obstacles that lead to program discontinuation, as well as various reasons for noncompletion. The reasons most often cited for non-completion in the literature include a lack of knowledge about the apprenticeship process, differing expectations between employers and apprentices, and poor employability skills (CAF, 2011b; Menard, Menzes, Chan & Walker, 2008; Stewart, 2009). There is also some evidence to suggest that employers and younger apprentices have differing expectations of each other, which may lead to conflict and become a barrier to long-term success in the workplace (Dooley & Payne, 2013; Stewart, 2009). However, there is insufficient information about the actual determinants of attrition from apprenticeship programs and there is evidence to suggest that numerous factors contribute to program discontinuation (CAF, 2004). The purpose of this study was to examine the effectiveness of an intervention program designed to increase apprenticeship retention and reduce some of the personal obstacles apprentices face to continuation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".