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Record W4407818316 · doi:10.1177/10482911251314149

The Point of No Return? Impediments to Return to Work for Injured Migrant Agricultural Workers in Two Canadian Provinces

2025· article· en· W4407818316 on OpenAlexafffundabout
Stephanie Mayell, Janet McLaughlin, Jenna Hennebry, Guillermo Ventura Sanchez, Pankil Goswami, Jill Hanley

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsConcordia UniversityMcGill UniversityWilfrid Laurier UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOccupational safety and healthWork (physics)Workers' compensationImmigrationBusinessBureaucracyCompensation (psychology)Demographic economicsMigrant workersAgricultureLabour economicsEconomic growthMedicinePolitical sciencePsychologyEconomicsGeographyEngineeringPoliticsSocial psychology

Abstract

fetched live from OpenAlex

Migrant agricultural workers employed through Canada's Temporary Foreign Worker Program face serious occupational health and safety hazards, with compounded difficulties in accessing workers' compensation (WC) if they are sick or injured by the job. Little is known, however, about their ability to return to work (RTW) upon recovery-a fundamental right included in the conception of WC, but complicated by their restrictive work permits and precarious immigration status. Based on interviews with injured migrant workers in two Canadian provinces (Quebec and Ontario), our research suggests that workers' RTW process is anything but straightforward. This article highlights three key issues-pressure to return to work prematurely, communication and bureaucratic challenges with WC agencies, and impacts of injury/illness and failure to return to work on workers' long-term well-being. Consequences and opportunities for reform are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.297
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueNEW SOLUTIONS A Journal of Environmental and Occupational Health PolicySame topicAgriculture and Farm SafetyFrench-language works237,207