Considerations of race and ethnicity within rehabilitation studies for post <scp>COVID</scp> ‐19 condition: A scoping review
Bibliographic record
Abstract
Post COVID-19 condition (PCC) or long COVID disproportionately affects racial and ethnic minority communities. There are a growing number of rehabilitation studies for PCC, however, it has yet to be determined whether existing studies take race and ethnicity into account in their study designs and whether existing rehabilitative approaches are equally effective across diverse racial and ethnic groups. The objective of this study was to describe the extent to which rehabilitation studies of PCC consider race and ethnicity in defining eligibility criteria, planning recruitment strategies, designing intervention delivery and adherence promoting approaches, selecting outcome measures, and reporting results. Of the 4845 studies screened, 23 met eligibility criteria and were included in this review. The most common reason for exclusion was a lack of mention of race or ethnicity anywhere within the article. Among the 23 studies included, 13 studies provided data on the race and/or ethnicity characteristics of their sample, with 88% of participants across all of these studies being White. Less than 25% of studies described the incorporation of race and/or ethnicity in their recruitment strategies (n = 3, 13%) or data analysis (n = 5, 22%). Greater racial and ethnic diversity is needed within rehabilitation studies for PCC as there is currently a significant underrepresentation of racial and ethnic minorities in existing studies. Overall, more PCC rehabilitation studies need to incorporate race and ethnicity into their study designs as it is not well understood whether existing rehabilitation strategies are equally effective across different racial and ethnic groups.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".