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Record W3091759657 · doi:10.1177/1539449220960062

Navigating Intersecting Forms of Oppression in the Search for Employment

2020· article· en· W3091759657 on OpenAlexafffund
Suzanne Huot, Perdita Elliott, Leanne Fells

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of British Columbia
FundersVancouver Foundation
KeywordsOppressionRefugeeStigma (botany)InjusticeLanguage barrierThematic analysisPovertyPsychologyPublic relationsPolitical scienceSociologyQualitative researchSocial psychology

Abstract

fetched live from OpenAlex

Opportunities for refugees to engage in occupations within host countries can be constrained by factors such as governmental policies or language barriers (Smith). Female refugees with physical disabilities may experience compounding barriers to participation related to their identity markers. The main aim of this study is to understand the experiences of female refugees with physical disabilities in seeking, preparing for, and obtaining employment. This study used an instrumental case study using semistructured interviews with five primary participants and four caregivers. Thematic analysis (Clarke & Braun) identified five key barriers to securing employment: (a) stigma and discrimination, (b) restrictive traditional labor market, (c) inaccessible and inadequate housing, (d) lack of cohesion and information across services, and (e) English language predominance. Findings highlight complex challenges experienced by the women who faced intersecting systems of oppression and can enable occupational therapists to address barriers associated with occupational injustice by improving services to better meet their needs.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0130.015
Scholarly communication0.0070.004
Open science0.0010.015
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.544
GPT teacher head0.639
Teacher spread0.094 · 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 designQualitative
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

Citations8
Published2020
Admission routes2
Has abstractyes

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Same venueOTJR Occupational Therapy Journal of ResearchSame topicOccupational Therapy Practice and ResearchFrench-language works237,207