85 Exploring reporting of ethno-racial identity and immigration status in published studies on children new to Canada: An integrative scoping review
Bibliographic record
Abstract
Abstract Primary Subject area Global Child and Youth Health Background Children immigrating to Canada may face racism and xenophobia depending on their ethno-racial background and immigration status. In Canada, immigration statuses include economic or family immigrants, resettled government or privately sponsored refugees, or asylum seekers, while some have no formal immigration status, otherwise considered undocumented. Research supporting newcomer child health should account for their immigration status and ethno-racial identity to capture the impact of discrimination. Objectives To critically examine the reporting of ethno-racial data and immigration status in published literature on the health needs of newcomer children to Canada. Design/Methods An integrative scoping review was performed, using the methodological framework outlined by Arksey & O’Malley. A literature search in Medline, PsycINFO, Scopus, Embase and Cochrane Central for articles published until July 2019 was conducted. Inclusion criteria were original research studies on newcomer children (0-18 years) in Canada in English or French from 2009 onwards. After undergoing title and abstract review, we extracted descriptions of participant immigration status and ethno-racial identity. Results 4147 articles were identified. After removal of duplicates, 2632 articles underwent title and abstract review, with a kappa-statistic of 0.93, suggesting high inter-rater agreement. Seventy-five studies were included in the final analysis. Overall, there were no consistent descriptions of immigration status or ethno-racial identity. Of the 75 articles included for final analysis, only 27% (20/75) described their participants’ immigration status in some capacity; the majority (75%) of these did not separate out participants by their immigration status (15/20) and of these, 67% combined all types of refugee and economic immigrant statuses together (10/15). With respect to ethno-racial data, the majority of studies (65%, 49/75) did not report on their participants’ ethno-racial identities. Of those that did, 65% (17/26) reported their participants’ ethnicity alone, while only 15% (4/26) reported their race alone and 19% (5/26) reported both race and ethnicity. Conclusion Our scoping review demonstrates that many studies focusing on newcomer children to Canada do not consistently collect and analyze their participants’ immigration status or ethno-racial identity. In doing so, studies may falsely conflate the experiences of newcomer children and ignore the impact of racism and xenophobia on their access to care, leading to worsening stigma and access to care. We suggest that research that often informs evidence-based guidelines for newcomer children should consider immigration status and ethno-racial identity to consider the impact of xenophobia and racism and improve health outcomes.
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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 | 0.044 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.054 | 0.057 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".