Listening to the Communities : Perspectives of Remote and Rural First Nations Community Members on Telemental Health
Bibliographic record
Abstract
Telemental health involves technologies such as videoconferencing to deliver mental health services and education, and to connect individuals and communities for healing and health. In remote and rural First Nations there are often challenges both to obtaining mental healthcare within the community and to working with external mental health workers. Telemental health is a service approach that can address some of these challenges and potentially support First Nations in their goal of improving mental health and well-being. This paper explores the perspectives on telemental health of community members living in two rural and remote First Nations communities in Ontario: Mishkeegogamang and Fort Severn. Using a participatory research design, we interviewed 59 community members, asking about their experiences with and thoughts on using technologies and their attitudes toward telemental health specifically. A thematic analysis of this qualitative data, and a descriptive quantitative analysis of the information reveal the diversity of attitudes among community members. Community members’ perspectives on the usefulness and appropriateness of telemental health greatly influence the level of engagement with the service. Valuing Indigenous knowledge can help us understand community members’ experiences of and concerns with telemental health and inform more successful and appropriate initiatives. We explore the continuum of community members’ perspectives – ranging from enthusiasm and embracing the technology use to hesitancy and rejection. With the invaluable support of the Keewaytinook Okimakanak Telemedicine co-authors (including the community telehealth coordinators), we offer ways forward to address concerns identified by the community members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".