Conceptualizing Stigma in the Injured Worker Literature: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Injured workers experience stigmatization, but the current literature has not applied a stigma lens to this demographic. Stigmatizing experiences are described, but not by readily using the term "stigma," making it difficult to locate these works. The purpose of this scoping review was to identify the terms and phrases that are being used to describe the stigmatizing experiences of injured workers. METHODS: A scoping review was conducted, searching MEDLINE, PsycINFO, and CINAHL for papers that described the stigma experiences of injured workers. The main objectives were to determine (i) whether the term "stigma" was used (and if it was a major or minor term) and (ii) what terms were used to describe these stigmatizing experiences. Post hoc, the terms were grouped into components of popular stigma theories (Attribution Theory, Modified Labeling Theory, and the Regressive Self-Stigma Model). RESULTS: 100 articles were included in the review. 48% of the studies used the term "stigma," but of these studies, only 11 (23%) used "stigma" consistently throughout their papers. There were 271 unique terms identified that described the stigmatizing experiences injured workers face, which most commonly described cognitive and behavioral forms of stigma. CONCLUSIONS: This review confirmed that a stigma lens has not been adopted to describe the experiences of injured workers, but that prominent theories of public and structural stigma explain these experiences well. This review also consolidated the various terms used to describe stigma experiences of injured workers, which will improve accessibility of the current literature for knowledge users and interested parties.
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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 | 0.022 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.029 | 0.025 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".