SOMALI-CANADIAN YOUTH: EMPLOYMENT, HEALTH, PANDEMIC
Bibliographic record
Abstract
Background: The first wave of Somali-Canadian refugees arrived in the 1990s following the civil war, with many settling in Toronto. First generation Somali-Canadians faced significant discrimination and settlement challenges. Previous research on Canada immigrants found that second generation youth tend to have a bright socioeconomic outlook. However, this outlook is not so certain for Somali-Canadian youth as they face unique long-term challenges with systemic barriers. Additionally, the pandemic has disproportionately impacted Black communities. Thus, it is important to explore how the pandemic has impacted the employment and health of Somali-Canadian youth living in Rexdale. Methods: Through an IPA approach, semi-structured interviews were conducted with 8 Somali-Canadian youth between the ages of 18-25 living in the Rexdale neighbourhood. The interviews were then analyzed through the IPA perspective to generate themes. Results: Somali-Canadian youth experience precarious employment, unsafe working conditions, lost income, faced financial difficulties and an increased risk of exposure to COVID-19. Furthermore, these impacts were exacerbated by living with large families and immunocompromised family members. Participants accessed government COVID-19 supports but some were ineligible due to precarious work or concerns associated with accessing governmental assistance. The pandemic negatively impacted the mental health of youth due to financial worries, educational and interpersonal challenges, and cultural stigma. Additionally, the intersectional identities of the participants resulted in limited choices and access to coping strategies. Conclusions: In order to improve the physical and mental wellbeing of Somali-Canadian youth, and those who share their experiences, there needs to be greater investments into the social determinants of health including employment, healthcare, housing, income, and education.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".