Immigrant Onboarding in Non-Gateway Quebec Small to Medium Enterprises
Bibliographic record
Abstract
Research Problem: Manufacturing small to medium enterprises (SMEs) outside of Montreal, Toronto and Vancouver are turning to immigrants and temporary foreign workers to fill a labour shortage. This study investigated SMEs onboarding programs to attract, integrate and retain, immigrants and their perceived efficacy from the perspectives of executives, supervisors and immigrant and non-immigrant workers to identify stakeholders' support and training needs \nResearch Questions: \n1.\tWhat onboarding strategies do non-gateway manufacturing SMEs use to recruit train and retain immigrant employees with technical or trade skills? \n2.\tWhat are the perceived challenges that non-gateway manufacturing SMEs encounter when trying to recruit, train and retain immigrant employees with technical or trade skills? \n3.\tHow do non-gateway manufacturing SMEs see immigrants as meeting their labour needs and what drives this vision? \n4.\tWhat are non-gateway manufacturing SMEs perceptions of government policies, and the immigrant integration services available to them and what additional support do they need to successfully onboard immigrant employees with technical or trade skills? \n5.\tWhat are the onboarding experiences and challenges of immigrants with technical or trade skills in non-gateway SME manufacturing companies? \nMethodology: Four manufacturing SMEs from different regions in the province of Quebec participated. Data was collected in three forms i) qualitative phenomenological interviews with six to eight employees per company, for a total of 28 participants, ii) company onboarding and community integration service artefacts and iii) field notes from company visits. \nResults and Conclusions: Onboarding programs are an iterative learning process, that should be framed by a culture of organizational learning, communication, teamwork, and where training is provided for both incoming and existing workers. Stakeholders can improve the attraction and retention of immigrants through the two Bienvenue Onboard models emerging from this study. The models are adapted to the needs of immigrants and temporary foreign workers and consist of seven iterative steps: i) Prepare an action plan, ii) advertise and recruit strategically, iii) prepare existing and incoming staff, iv) offer workplace and job orientation programs, v) offer social integration support, vi) follow-up with further investments in people and vii) evaluate the onboarding experience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".