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Record W2626599847 · doi:10.1177/0008417417701229

Stigma and work discrimination among cancer survivors: A scoping review and recommendations

2017· review· en· W2626599847 on OpenAlexfundvenueno aff
Mary Stergiou‐Kita, Xueqing Qie, Hau Ki Yau, Sally Lindsay

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2017
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le Cancer
KeywordsPsycINFOCINAHLStigma (botany)HarassmentEmployment discriminationEmployabilityPsychologyMEDLINEMedicinePsychological interventionSocial psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Stigma and workplace discrimination can hinder employment opportunities for cancer survivors. PURPOSE: This study explored perceptions of stigma and workplace discrimination for cancer survivors to understand the impact on survivors' engagement in paid work and to identify strategies to address stigma and workplace discrimination. METHOD: Using Arksey and O'Malley's framework, we searched Medline, Embase, PsycINFO, Scopus, and CINAHL for evidence that intersected three concepts: cancer, stigma, and employment/workplace discrimination. Of the 1,514 articles initially identified, 39 met our inclusion criteria. Findings were charted, collated, and analyzed using content analysis. FINDINGS: Myths regarding cancer (i.e., it is contagious, will always result in death) persist and can create misperceptions regarding survivors' employability and lead to self-stigmatization. Workplace discrimination may include hiring discrimination, harassment, job reassignment, job loss, and limited career advancement. Strategies to mitigate stigma and workplace discrimination include education, advocacy, and antidiscrimination policies. IMPLICATIONS: Occupational therapists can enhance awareness of workplace concerns and advocate on behalf of cancer survivors.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.313
GPT teacher head0.484
Teacher spread0.171 · 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 designOther design
Domainnot available
GenreReview

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

Citations38
Published2017
Admission routes2
Has abstractyes

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