Creating an Empirically Informed Mental Health Workbook for Racialised Newcomer Youth in Saskatchewan: Community‐Based Participatory Action Research
Bibliographic record
Abstract
Immigration is a significant determinant of mental illness among racialised newcomer youth who experience immigration challenges, including cross-cultural transitions and adaptations, social exclusion, anti-immigrant policies and the loss and reconstruction of social support networks. The development of tools to support self-care can improve the mental health and well-being of this population. In this project, we utilised the photovoice approach to explore racialised newcomers' mental health struggles and how they mitigate them. We constituted a working group comprising racialised newcomers with lived experiences of immigration-related mental health stressors to support the workbook development. The risk for mental illnesses for international students, and signs and symptoms of stress, depression and anxiety were covered. We utilised photos that depicted the immigration-related mental health stressors and mental health boosters that help mitigate these stressors. Each photo had a title and a closed caption that depicted its essence. Spaces to journal were provided as well as a list of resources for free groceries, religious organisations and thrift shops. The workbook was pilot tested with 13 participants who were racialised newcomers and experienced self-reported symptoms of mental illness since arriving in Canada. Their reported benefits of utilising the workbook include increasing mental health literacy, validating experiences, creating catharsis through journaling, incentivising them to better respond to stressors and habitually developing practices to boost their mental well-being. A workbook with psychoeducation content on mental health risks and symptoms and content that utilises data from people with experiences like the targeted population can enhance the agency of self-care through increased knowledge, validation of experiences and inculcation of hope.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".