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Record W4293490253 · doi:10.1089/heq.2022.0006

Undocumented Americans Need Equitable Language in Worker Training

2022· article· en· W4293490253 on OpenAlexaff
Eric Persaud

Bibliographic record

VenueHealth Equity · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsYork Central Hospital
Fundersnot available
KeywordsImmigrationCurriculumMedical educationPandemicGerontologyPsychologyMedicineCoronavirus disease 2019 (COVID-19)Public relationsPolitical sciencePedagogyInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Dear Editor: Undocumented immigrant American workers face barriers to adequate safety training and disparities in occupational health.1 The workplace plays a vital role in the lives of all Americans who perform the necessary work that keeps society functioning, including undocumented workers. It is important that workers receive training and education to perform their work safely and in a healthy manner. Since the COVID-19 pandemic, there has been a shift from in-person training and education to virtual platforms due to the need to be socially distant, including occupational health and safety (OHS) training. There was a need to integrate COVID-19 and infectious disease control and prevention curricula into broader worker training programs. Some of those OHS trainings were conducted virtually, despite barriers that existed for many, including immigrants and persons of color, in technological access, comfort, and fluency.2 Undocumented workers, often immigrants and persons of color, filled many of the roles of essential workers, and were disproportionately employed to work in-person during the COVID-19 pandemic in unsafe working conditions and environments.3 It remains unclear the effectiveness of OHS training undocumented workers received in response to the COVID-19 pandemic and in some cases if any OHS training was even offered. Public health practitioners, researchers, and program planners need to further recognize how the COVID-19 pandemic has created changes in training and education for undocumented workers, who were already facing limitations in virtual and in-person services. At a minimum, the language for OHS training needs to be appropriate for the audience and competently delivered by the instructor.4 Language, however, is not just the dialect, for example, English, Spanish, or Vietnamese, but also using the words in a context the trainees understand.5 For example, undocumented workers may fear reporting injuries on the jobsite and unsafe working conditions, despite their right to do so under federal law without employer retaliation. Simply stating a worker can report injuries may not be enough for undocumented workers. Clarifying worker rights against employer retaliation regardless of their documentation status is important and should be emphasized in OHS training programs.1 This is one of many such examples of how language of worker rights to safety and health needs to be equitable to undocumented workers. There is a need to act toward ensuring that language equity is part of OHS training. Instructors need to engage those with lived experiences of undocumented status and advocate for such audiences to support review of curricula. The input of those with lived experiences can provide context and content an instructor may not have otherwise. Instructors should develop and deliver OHS training on a platform and in a language participants understand, provide translation of materials into languages that fit possible target audiences, and use terminology framed in a practical context applicable to the target audience. To do so would better prepare and protect all workers, including our undocumented workers who equally deserve and need safe working conditions and environments.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0140.016
Insufficient payload (model declined to judge)0.0200.006

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 head0.247
GPT teacher head0.563
Teacher spread0.316 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2022
Admission routes1
Has abstractyes

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