Provision of employment-related settlement services and relationship with paid employment for immigrants in Canada
Bibliographic record
Abstract
Immigrant workers are overly represented in high risk and precarious jobs that are not commensurate with their background, skills and experience. Some evidence exists to suggest that access to employment-related (ER) supports and services in the community can help leverage job opportunities. This study examined the association between use of government-funded ER services and paid employment of immigrants to Canada. ER service records were linked with immigration and taxation records for individuals who immigrated to Canada between 2015-2017. The cohort was restricted to immigrants with no paid employment in their year of landing to examine the direct impact of ER service (measured as any ER service, intensity of service and type of ER service) on subsequent employment. The outcome of subsequent employment in the year following ER service provision was estimated using adjusted logistic regression models. Immigrants displayed a higher odds of paid employment the year following the ER service for individuals that accessed any ER service (OR = 1.57; 95% CI, 1.49 to 1.65), across the measures of ER intensity (ranging from OR = 1.26; 95%CI, 1.09 to 1.45 at the lowest intensity percentile to OR = 2.21; 95% CI, 1.95 to 2.52 at the highest intensity percentile), and by type of ER service (essential skills and aptitude training, OR = 1.35; 95% CI, 0.93 to 1.96; short-term intervention, OR = 1.57; 95% CI, 1.49 to 1.66; long-term intervention, OR = 1.45; 95% CI, 1.07 to 1.95) compared to immigrants who did not access ER services. This study finds that access to an ER service is associated with paid employment. It highlights key services that should be promoted to facilitate employment integration but also potential barriers to integration that warrant further investigations.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".