Stigma and work discrimination among cancer survivors: A scoping review and recommendations
Bibliographic record
Abstract
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 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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".