MENTAL WELLNESS NEEDS AND TECHNOLOGICAL RESOURCING WITH THE MI’KMAQ POPULATION OF PEI: A JOURNEY OF RESEARCH PROCESS, DISCOVERY, OPPORTUNITY, AND GROWTH
Bibliographic record
Abstract
Indigenous populations in Canada experience more mental wellness challenges compared with their non-Indigenous counterparts so it is critical to explore the most appropriate forms of interventions and care needed to support the mental wellness of Indigenous populations. Due to disparities in access to care experienced by many Indigenous communities, there are potential opportunities for technology to support mental wellness of Indigenous populations. In this dissertation, I detail my journey working with community to illustrate culturally responsive research and to explore how technology could address mental wellness needs. This research used community-based participatory research and a Two-Eyed Seeing philosophy. Results from Study 1 revealed that the wellness needs of two Mi’kmaq First Nations communities (Abegweit and Lennox Island) are divided into three levels that include the individual, the community, and the larger system; and addressing all three levels would be required to improve mental wellness. Study 2 was conducted with the Abegweit First Nations community and focused on views on technology to support wellness. Results from Study 2 revealed tensions in the use of technology to support mental wellness and provided insight into ways to approach and include the First Nation’s community in a technologically advancing world. These findings were gifted by communities who were controlling and determining the direction of this journey. These conversations revealed evidence of between- and within-group variabilities; the possibility of an Indigenous researcher being too much of a community insider; technology requiring balance and caution when working in communities; and shared responsibility of supporting mental wellness in terms of both individual understanding to system level reconciliation. This dissertation is an example of how to connect with these specific communities in a way that will increase the likelihood of successful support, services, and resource uptake. It is hopeful that this journey, on a larger scale, will encourage researchers to consider unique circumstances of Indigenous communities as well as the unique circumstances of individuals in those communities to allow more effective collaboration.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.010 | 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".