The employment preferences of young people in Canada: a discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: Young people across the world are facing numerous challenges, with unemployment and precarious employment being substantial issues, impacting young people with all levels of education. For many young people, the pandemic exacerbated their employment precarity. While efforts were made to ameliorate these pandemic related challenges for young people, information about the employment preferences of Canadian young workers (YW) is limited. The aim of this study was to understand the employment needs, challenges and preferences of Canadian YW in the COVID-19 era and beyond. METHODS: Using discrete choice experiment, YW from across Canada aged 18-29 years old were recruited to participate in an online survey October 2022 to April 2023 which was offered in both English and French. Nine job attributes were identified based on findings from the qualitative component of this mixed methods project: wage, earnings stability, job flexibility, vacation, sick time, health insurance, and workplace policies (respectful workplace, and being valued and understood as an employee). Respondents were presented with nine choice sets, each representing two scenarios that differ on policies or actions (attributes) related to their employment during the COVID-19 pandemic. RESULTS: Based on the respondent (N = 231) sample, analysis revealed that of YW aged 18-29 years, most valued having employment benefits along with workplace policies. These values were strongest for women and 18-21-year-olds. Overall, the employment preferences of Canadian YW in the current study align with four of five attributes considered by the International Labour Organization as minimum standards for decent work. These include adequate compensation, adequate access to health care, adequate free time and rest, and organizational values that support one's [own and] family values. More specifically, study findings show that within the cohort there are strong gendered and aged-based preferences for non-monetary over monetary job attributes. These include employment benefits along with equitable, supportive employment policies. CONCLUSIONS: The findings suggest that health and wellbeing are highly valued by YW and are among key drivers of employment preferences for Canadian YW during and after the pandemic, and therefore call for policies in the workplace that support the health and well-being of YW.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".