Imagining an Equitable Mental Health Ecosystem: Co-designing with Immigrant Women to Encourage Mental Health through Nature Interaction in an Urban Context
Bibliographic record
Abstract
Immigrants currently represent a quarter of the Canadian population, and this continues to increase as more people move due to social, financial, political, and environmental causes. However, this population experiences a considerable decline in their health over time upon their arrival; thus, making immigrant health a crucial public health issue. Immigrant women in specific experience a variety of stressors including employment, family support, and cultural shock regarding gender roles, which put their mental health at high risk. Therefore, mental health inequities should be tackled by putting equity and intersectionality front and center. The wide amount of literature supporting the benefits of nature interaction on mental and physical health (Birch et al., 2020; Kotera et al. 2020; Lorentzen & Viken, 2022; Shanahan et al., 2015) makes it a promising method to support the mental health of immigrant women, as well as the overwhelmed health system. Based on the complexity of the health issue identified, this design research project aimed to use a participatory approach to gather key insights from immigrant women to acquire an understanding of the needs and potential interventions to achieve accessible nature interactions for public mental health. For this study, twenty-one immigrant women from various geographical backgrounds living across the Greater Toronto Area were recruited to participate in one-on-one interviews and two co-design sessions. These activities revealed that immigrant women indeed experience positive emotions as a result of nature interactions. However, they also experience barriers like distance, time, and lack of information that keep them from engaging with nature more often. Furthermore, their participation also uncovered key areas of concern in the immigrant experience, which contribute to a high amount of daily stress, and little to no time for self-care and emotional relief. Using systems design tools during the co-design sessions served to identify the main areas of challenge for this population across the system, as well as specific values and practices that participants would like to see across system scales. The emergent character of the design research approach used, led to the development of an Equitable Mental Health Ecosystem model (see Figure 1). This model is a service design ecosystem that could facilitate equitable mental health by providing alignment to mental health providers and major funding bodies through three sets of foundational values and practices that guarantee equitable and intentional support for a diverse population. Acknowledging the mental and social burdens behind the immigrant experience, adopting the proposed model can be life-changing to those populations that for decades, have encountered barriers in their mental health-seeking journey. In conclusion, this study approaches a complex community health topic to propose a service ecosystem model that attempts to make mental health care accessible, resilient, community-driven, sustainable, and equitable.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".