Participation of Racialized Individuals in Healthcare Research: A Scoping Review Protocol
Bibliographic record
Abstract
Introduction: Despite a lack of formal requirements for recruiting racialized populations in Canadian healthcare research, incorporating racial diversity is crucial for improving the generalizability and effectiveness of healthcare interventions. By including diverse participants, healthcare solutions can also benefit historically marginalized racial groups, thereby helping to reduce health inequities within Canada’s healthcare system. To better address this gap, this proposed scoping review aims to map all barriers and facilitators to the participation of racialized groups in healthcare research. Objective: The objective of this review is to identify existing literature on the barriers and facilitators to the participation of racialized populations in healthcare research. Methods: Our scoping review will follow guidance from the JBI scoping review methods manual. Five databases will be searched (PubMed, EMBASE, Scopus, CINAHL, PsycINFO). Grey literature will be searched using ProQuest Dissertations and Theses, Google Scholar, and Canadian Agencies for Drugs and Technologies in Health (CADTH)’s Grey Matters online tool. The search duration will include all studies from database inception to February 2024. Data extraction will be conducted by two independent reviewers using a data extraction tool developed a priori and pilot-tested by the reviewers. A data chart- ing table was developed using guidance from the JBI manual. The following data will be extracted from each article: study title, author, country of origin, year of publication, study type, participants (including sample size and racial identity if pro- vided), concept, context, outcome(s), and results pertaining to study objectives. Results will be presented through a narrative summary which will map barriers and facilitators within micro-, meso-, and macro-levels of society.
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 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 | 0.219 | 0.154 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.066 | 0.021 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".