Uncovering systemic barriers related to equity, diversity and inclusion in child health research: a scoping review addressing marginalised communities
Bibliographic record
Abstract
INTRODUCTION: Despite abundant evidence illustrating the impact of social determinants of health on children and youth from marginalised communities, their continued marginalisation in research contributes to a negative feedback loop that perpetuates health inequities. Previous reviews have identified barriers in marginalised adult communities. However, no comprehensive review outlines the scope of barriers to equity, diversity and inclusion (EDI) in child health research across multiple marginalised communities, particularly as they are defined in Canada. METHODS: The purpose of this review is to scope and summarise research discussing systemic barriers influencing EDI in child health research, focusing on racialised and black individuals, 2SLGBTQIA+ individuals, Indigenous peoples, women and girls and individuals with disabilities (identified as priority communities in the Canadian government's research policy). Our team followed the steps proposed by Arksey and O'Malley for scoping reviews. RESULTS: From 3336 identified records, 53 publications met the inclusion criteria. Most studies were based in the USA (n=40) and/or other English-speaking countries (n=14), emphasising the need for global perspectives. Some publications were based in more than one country; others addressed more than one marginalised community. We identified more publications discussing racialised individuals (n=30) and black individuals (n=20) than women and girls (n=10), Indigenous peoples (n=9), children with disabilities (n=7) or 2SLGBTQIA+ individuals (n=4). Publications increased from 3 in 2020 to 15 in 2022, reflecting heightened awareness of structural racism and health inequities during the COVID-19 pandemic. Our findings suggest systemic under-recruitment and tokenism. Other factors in the research ecosystem include misleading conceptualisations of race and other social categories, power dynamics, lack of cultural safety and discrimination. Finally, we recommend applying the socio-ecological model to systematically map barriers and develop tailored, multilevel solutions that promote equity and inclusivity in research. CONCLUSION: To foster a more equitable and impactful child health research ecosystem, institutions must address individual, interpersonal, organisational and policy-level barriers by embedding community-driven priorities, promoting diverse and inclusive practices, and ensuring long-term, reciprocal relationships with historically marginalised communities.
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 | 0.062 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".