Cultural Safety in Service Provision for Migrant Agricultural Workers? An Empirical Exploration
Bibliographic record
Abstract
Activists and scholars have long highlighted the precarity of migrant agricultural workers (MAWs) in Canada and the unique threat it poses to this group’s health and wellbeing. In response, the Canadian government recently launched a series of targeted programming and services for this population. However, little research has explored the effectiveness of these initiatives within the broader support landscape. Our qualitative narrative research involving 165 MAWs in Southern Ontario aimed to explore the extent to which support services (i.e. health, legal, community) mitigate and comprehensive address MAWs’ key needs, as well as the mechanisms through which these support organizations achieve this. Drawing on the concept of cultural safety and the collective voices of participants, our team identified two key dimensions to inform a more comprehensive approach to service delivery for this population: alignment, which focuses on priority needs, and accessibility, which anticipates and mitigates barriers to help-seeking. Our work points to the value of expanding how we envision service provision, while simultaneously addressing the intricate structural inequities that create the need for accompaniment in the first place. A cultural safety approach necessitates situating temporary migrant programs and their service provisions within political and historical contexts while advocating for sustained support frameworks and fostering deeper engagement with MAWs, who ultimately determine when cultural safety is achieved.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".