Applying intersectionality in vision impairment research: A scoping review
Bibliographic record
Abstract
There are calls for better application of theory in health research. Applying intersectionality theory in vision impairment research is critical because it affords an in-depth understanding of social issues, including their causes. Explicit application of intersectionality theory can further enhance research and practice in vision impairment; yet, there is a paucity of research on how intersectionality theory is applied and the degree to which it can guide vision impairment research. The purpose of this scoping review was to understand how intersectionality theory has been applied within vision impairment research and how it can be used to guide further vision impairment research development. A scoping review was conducted to examine and summarize the extent, range, and nature of the application of intersectionality theory within vision impairment research. Four electronic databases were searched from inception in April 2023, resulting in 1632 unique records. Inclusion/exclusion criteria were applied, resulting in 19 articles being identified for further analysis. The application of intersectionality theory in vision impairment research was seen most frequently among authors in the field of anthropology and human and movement science. The way in which intersectionality theory was taken up in vision impairment research is described using three overarching themes including: (1) as a lens for the interpretation of findings; (2) as a general conceptual framework for the article; and (3) as a tool for data analysis.
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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 | 0.051 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.035 | 0.041 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".