Editorial: Diversity, Equity, and Inclusion: Reporting Race and Ethnicity in the <i>Journal of Pediatric Psychology</i>
Bibliographic record
Abstract
Events over the past year related to the civil unrest, protests, and riots against systemic racism toward Black people in the United States, including the police killings of Breonna Taylor, Rayshard Brooks, and Elijah McClain, the modern-day lynching of Ahmaud Arbery, and the international catalyst triggered by the public murder of George Floyd during his arrest by Minnesota police officers, has brought an urgency in addressing racism in various contexts. These tragic injustices, only brought to light because bystanders courageously captured videos, represent just the tip of the iceberg of deeply entrenched racism that penetrates all aspects of life, institutions, and judicial and healthcare systems around the world. Of relevance to the Journal of Pediatric Psychology (JPP) are urgent calls to upend racism in psychological science (Buchanan et al., 2020; Roberts et al., 2020), which can occur in conducting, reporting, reviewing, and disseminating science. As highlighted by several scholars (e.g., Buchanan et al., 2020), a dismantling of White supremacy is needed at the systems level because current practices are biased toward White scholars and White communities, and thus are not representative. If changes are not made, it reduces the potential impact of psychological science to make meaningful progress in diminishing persistent physical and mental health disparities across racial and ethnic groups. Journals play an important role in this gatekeeping process where the actions of editors and reviewers determine which science is disseminated and shape how the science is conducted and reported. JPP joins other biomedical and psychology journals (e.g., Novins et al., 2020; Pachter, 2020) in its commitment to being an anti-racist journal. We acknowledge that JPP must do more to increase representation in pediatric psychology research, to support diversity, and to encourage the use of frameworks that explicitly incorporate social context (Matsui et al., 2020). To accomplish this goal, an anti-racist workgroup was formed from members of the JPP editorial team including the Editor, Associate Editors, and Assistant Editors. This editorial reports on the first project of the workgroup—to develop author and reviewer instructions for the reporting of race and ethnicity in papers submitted to JPP. To guide this effort, our workgroup reviewed key editorials, guidelines, and literature concerning bias-free language and reporting of race and ethnicity (e.g., Flanagin et al., 2021; Miller et al., 2019). We also consulted with experts and other groups performing similar work. The purpose of this editorial is to describe the recommendations of the workgroup and to provide the new author and reviewer instructions for reporting race and ethnicity, including the use of bias-free language. The new author instructions are listed below verbatim and are also available on the JPP web site, https://academic.oup.com/jpepsy/pages/author_instructions. Our goals with these instructions are to: (1) ensure fair and unbiased reviews; (2) publish manuscripts that use bias-free language; (3) require authors to comprehensively report race and ethnicity of their samples; and (4) require authors to consider race and ethnicity as social constructs in their interpretation of study findings. Race and ethnicity are social constructs couched within a sociopolitical framework. Race and ethnicity are not genetic or biological categories. Care should be taken in the methods used to characterize samples in regard to race and ethnicity, reporting of this information, and interpretation of findings related to race and ethnicity categories. Reporting of race and ethnicity (and associated intersectional factors such as culture and social structures) in the manuscript may vary across countries, languages, and cultures. Authors should provide sufficient rationale and justification for their data collection and reporting of race and ethnicity of their sample to be understood and appreciated by an international readership. Terminology. Authors should follow the APA Style Guidelines on Bias-Free Language https://apastyle.apa.org/style-grammar-guidelines/bias-free-language. These contain both general guidelines for writing about people without bias across a range of topics and specific guidelines that address the individual characteristics of age, disability, gender, participation in research, racial and ethnic identity, sexual orientation, socioeconomic status, and intersectionality. Terms used to describe racial and ethnic groups (including spelling and capitalization) should adhere to bias-free language for Racial and Ethnic Identity, https://apastyle.apa.org/style-grammar-guidelines/bias-free-language/racial-ethnic-minorities. The complexity of labeling is also addressed in the document with suggestions to use the racial and ethnic terms that your participants use. For example, instead of categorizing participants with a general label such as Asian American, a more specific label that identifies their nation or region of origin could be used instead such as Japanese American. Similarly, authors should use systems centered language, showing awareness that disparities are due to inequities or deficiencies in social structures, systems, and processes rather than individual weaknesses or choices; for example, rather than stating that a population is “vulnerable” or “at risk”, identify the harms or social structures that drive oppression and racism (see https://psyarxiv.com/6nk4x/ for further details). Source used to identify race and ethnicity. Clearly state the categories used to collect race and ethnicity data (e.g., Census data categories, funding agency categories) and the source of this information (e.g., participant self-report, electronic health record). Please indicate why those sources/categories were chosen (e.g., specified by the funding agency). For example, an author may state: “Reporting race and ethnicity in this study was mandated by the National Institutes of Health, consistent with the Inclusion of Women, Minorities, and Children policy”. Reporting race and ethnicity for sample description. Race and ethnicity of the study population should be reported in full in the Results section and/or in a participant characteristics table, as applicable. All race and ethnicity categories represented in the sample should be reported individually rather than collapsing data across groups (e.g., “Other”). Note: This reporting requirement does not dictate how race and ethnicity categories are used in analyses—authors may conduct statistical analyses with race and ethnicity variables combined as appropriate to their study goals and methods, with appropriate rationale. Interpretation of race and ethnicity findings and recognizing limitations. Avoid making assumptions and conclusions that whiteness is the norm; for example, do not assume White comparison groups are needed or that racial differences found in one group are abnormal in comparison to White individuals. For further details, see https://psyarxiv.com/6nk4x/. Consider the structural effects of racism, and histories of exclusion, mistreatment, and exploitation on the populations included in the research and/or in relation to the findings. Authors should avoid making conclusions that may be interpreted as placing blame on minoritized populations. As it relates to interpreting the study findings, racism should be named. Authors are encouraged to identify the form (interpersonal, institutional, systemic), the mechanism by which it may be operating, and other intersecting forms of oppression (such as based on sex, gender, sexual orientation, age, regionality, nationality, religion, or income) that may compound its effects. For further details, see: https://www.healthaffairs.org/do/10.1377/hblog20200630.939347/full/. Authors should clearly acknowledge the limitations of samples due to their lack of racial and ethnic representation (e.g., limited generalizability due to homogeneity of the sample). If data on racism or discrimination were not collected, authors should acknowledge this limitation in the interpretation of their findings. For further information on available scales assessing discrimination see https://scholar.harvard.edu/files/davidrwilliams/files/measuring_discrimination_resource_june_2016.pdf. Reviewers play a critical role in the implementation of our new instructions. As such, we want reviewers to be familiar with the author instructions to ensure their comments are consistent with them. Reviewers will be asked to comment on whether the manuscript appropriately addresses race and ethnicity and uses bias-free language as described by the author guidelines. There will be a learning curve—this may be a significant change from how race and ethnicity have previously been reported and interpreted. Reviewers are encouraged to contact the managing editor with any questions or concerns. Managing editors may also reach out to reviewers to gain clarity when comments or concerns are raised. In closing, efforts in diversity, equity, and inclusion at JPP are expected to evolve and to align with broader uniform requirements that may develop in the future for psychology and biomedical journals. We also highlight that race is one part of social identity that is relevant to the health of children and families—other aspects of identity (such as gender or sexuality) may also subject youth to inequities and oppression that are important to understand and combat (Azmitia & Mansfield, 2021). We recognize that there is much work to be done and encourage continued efforts and dialogue in this area by future editors, reviewers, and authors. Conflicts of interest: None declared. The authors thank Idia Thurston, PhD for her inspiration and for reviewing drafts of the author instructions.
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 | 0.021 | 0.096 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.029 | 0.029 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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".