6.10-P1Distress or resilience? Examining socio-spatial relations impacting the wellbeing and emotional health of seasonal agricultural migrant workers in rural Canada
Bibliographic record
Abstract
In Canada, nearly 50 000 migrant agricultural worker positions were available on temporary work permits in 2016. While the wellbeing of future Canadian citizens is considered an issue of national importance, health inequalities among the agricultural migrant workers seem to be increasing which pose challenges for public health management. This is particularly true for the health of Mexican migrant workers participating in the Canadian Seasonal Agricultural Workers Program (SAWP) who have been recognised as a precarious labour force and one of the longest standing group of circular migrants. These workers face higher risks of work-related illnesses and injuries within the agricultural industry. In rural areas, where greater obstacles to migration may exist, they are often exposed to abuse, discrimination, and exploitation in addition to the emotional and mental health distress of working away from their home community. The objectives of this poster are twofold: First: Relying on case studies, five in-depth interviews, and participant observation data from Quebec, Ontario, and Mexico, we analyse the data pertaining to the health of these temporary migrant workers according to distinct life stages and locations. Second: We chart the pathways and barriers encompassing the physical and psycho-emotional health-contingent on social, cultural, economic, political and environmental factors operating at different scalar levels affecting migrant workers’ lives in both their place of origin and temporary-working locations. The findings contribute to an understanding of the hazardous daily working conditions and their impacts on the physical and psycho-emotional well-being of racialised-workers while in Canada and their country of origin, which manifests the spaces of risk in transnational/cross-border places.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".