A scoping review on the operationalization of intersectional health research methods in studies related to the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic began in early 2020 and became a global health crisis with devastating impacts. This scoping review maps the key findings of research about the pandemic that has operationalized intersectional research methods around the world. It also tracks how these studies have engaged with methodological tenets of oppression, comparison, relationality, complexity, and deconstruction. METHODS: Our search resulted in 14,487 articles, 5164 of which were duplicates, and 9297 studies that did not meet the inclusion criteria were excluded. In total, 14 articles were included in this review. We used thematic analysis to analyse themes within this work and Misra et al. (2021) intersectional research framework to analyse the uptake of intersectional methods within such studies. RESULTS: The research related to the COVID-19 pandemic globally is paying attention to issues around the financial impacts of the pandemic, discrimination, gendered impacts, impacts of and on social ties, and implications for mental health. We also found strong uptake of centring research in the context of oppression, but less attention is being paid to comparison, relationality, complexity, and deconstruction. CONCLUSIONS: Our findings show the importance of intersectional research within public health policy formation, as well as room for greater rigour in the use of intersectional methods.
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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 | 0.086 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| 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".