A Narrative Inquiry of East Asian Parents and Mental Health in Canada: Critical Openings for Anti-Racism Strategies in Knowledge Translation
Bibliographic record
Abstract
BackgroundAnti-Asian racism is linked with adverse mental health conditions in young East Asian populations. There is a need to explore how to develop mental health resources for East Asian parents, yet minimal research explores anti-racism strategies for this work.PurposeThe objectives were to: open a critical dialogue for developing anti-racism strategies for mental health knowledge translation (KT) resource development, and explore complexities with engaging East Asian parents when developing KT resources.MethodsA narrative inquiry was conducted to collect East Asian parent stories on anti-racism strategies and mental health. East Asian parents across Canada engaged in semi-structured interviews between August to October 2022. Dialogic/performance analysis was used to inductively analyze the data. Findings: Three composite counter-narratives emerged from the data: 1) Storying issues of access within child mental health KT; 2) Seeking understanding and solidarity for the East Asian identity and story; 3) Unlearning, breaking barriers, and storying resistance. The composite narratives wove together seven storylines: a) availability and affordability, b) language and vocabulary barriers, c) lack of representation, d) issues of representation: power and whiteness, e) East Asian standpoint epistemology, f) breaking cycles, g) culture as a source of strength.ConclusionThe findings highlighted the complexities of engaging East Asian parents and recommended the need for an East Asian standpoint epistemology when developing child mental health KT resources and counter-spaces as a way to facilitate the centrality of East Asian standpoint epistemologies. These anti-racism strategies may promote solidarity for shared experiences beyond the white gaze and spaces.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".