Understanding mental health and wellbeing among aboriginal postsecondary students
Bibliographic record
Abstract
First Nations, Inuit and Métis youth in Canada report higher rates of mental health (i.e., depression, anxiety, addiction and suicide) issues than the non-Aboriginal population of youth, which impacts their ability to achieve their educational goals. Understanding mental health from an Aboriginal worldview and through culturally safe strategies designed to translate mental health information to Aboriginal people is lacking. The objectives of the present study were to develop, implement and evaluate a knowledge translation (KT) strategy to enhance wellbeing knowledge and self-efficacy in coping with mental health issues among Aboriginal postsecondary students using a community-based approach. The Tool Development Phase involved focus groups and interviews with Aboriginal post-secondary students (n=9) and community members (n=3) to develop a KT activity. The KT Phase involved a pre/post design where participants (n=4) were asked about their mental health knowledge and self-efficacy in improving their wellbeing prior to and after the KT activity. All students participated in an individual interview one month later about their knowledge and the helpfulness of activity. Key informants (n=4) were interviewed during a Follow-Up and Debriefing phase regarding the community-based approach. Mental health was defined wholistically, as related to community and as being impacted by identity, stigma, cultural connection, intergenerational trauma, and a clash between Aboriginal and positivist Western worldviews. KT preferences included incorporating traditional ceremonies; covering particular topics such as resources, traditional teachings and coping strategies; and incorporating active participation. The KT activity increased knowledge and self-efficacy related to mental health and some knowledge was retained and used at one-month follow-up. As well, participants appreciated the sense of community that the activity created and provided feedback regarding what could be changed (e.g., more active participation). Key informants thought the project addressed some community needs and that it was built on relationships. This study will contribute to increasing the wellbeing of Aboriginal students by expanding upon their mental health knowledge, and sense of self-efficacy in coping with their own mental health issues, with the aim of addressing mental health barriers to the completion of post-secondary education among Aboriginal students.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".