Impact of limited language proficiency on participation in venous thromboembolism research: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND: Limited language proficiency is an established barrier to research participation among racialized populations. While prior studies have highlighted the underrepresentation of racialized populations in venous thromboembolism (VTE) research, the impact of limited language proficiency as a reason for nonconsent among eligible patients is unknown. OBJECTIVES: To determine the impact of language barrier as the primary reason for VTE research non-participation. METHODS: We reviewed all prospective VTE studies conducted at a research-intensive academic thrombosis research program in Canada between 2014 and 2024. Studies with screening logs that systematically and consecutively captured eligibility assessment and reasons for nonconsent were included. Primary outcome was nonconsent of a screen-eligible patient due to limited language proficiency as the reported reason. We derived pooled estimates of nonconsent due to limited language proficiency as a proportion of consented participants and determined subgroup rates by phase of VTE management, associated medical conditions, and recruitment settings. RESULTS: Screening logs of 28 studies with 22 057 screening events, 8317 screen-eligible patients, and 3320 consented participants were included. For every 100 consented participants, 3.2 (95% CI, 2.0-5.3) screen-eligible individuals were unable to provide consent due to limited language proficiency. Rates of nonconsent were highest in studies involving cancer (5.6 per 100 participants; 95% CI, 2.9-10.4) and in studies recruiting patients from ambulatory settings outside of the thrombosis clinic (10.8 per 100 participants; 95% CI, 4.8-22.6). CONCLUSION: Language proficiency is a key barrier to VTE research participation. Urgent implementation of targeted interventions aimed at mitigating linguistic barriers is essential to ensure equitable opportunities for VTE research participation for racialized patients disproportionately affected by language proficiency.
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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 | 0.053 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".