Conversations on telemental health: listening to remote and rural First Nations communities
Bibliographic record
Abstract
INTRODUCTION: 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 communities there are often challenges to obtaining mental healthcare in the community and to working with external mental health workers. Telemental health is a service approach and tool that can address some of these challenges and potentially support First Nations communities in their goal of improving mental health and wellbeing. Community members' perspectives on the usefulness and appropriateness of telemental health can greatly influence the level of engagement with the service. It appears that no research or literature exists on First Nations community members' perspectives on telemental health, or even on community perspectives on the broader area of technologies for mental health services. Therefore, this article explores the perspectives on telemental health of community members living in two rural and remote First Nations communities in Ontario, Canada. METHODS; This study was part of the VideoCom project, a collaborative research project exploring how remote and rural First Nations communities are using ICTs. This current exploration was conducted with the support of Keewaytinook Okimakanak (KO), our partner in Northwestern Ontario. With the full collaboration of the communities' leadership, a team involving KO staff and VideoCom researchers visited the two communities in the spring of 2010. 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 revealed the diversity of attitudes among community members. Finally, based on a discussion with the community telehealth staff, a 'ways forward' section was proposed as a way to begin addressing certain issues that were raised by community members. RESULTS: This article explores the continuum of community members' perspectives that range from interest and enthusiasm to hesitancy and concern. One participant reported personal experience with using telemental health and found the approach helpful in increasing her comfort in the therapeutic situation. In addition, concerns relating to appropriateness and safety were voiced. A variety of advantages (eg facilitation of disclosure, increased access to services, usefulness) and disadvantages or concerns (eg interference with capacity building, concerns about privacy) are reported and discussed. Following a coding procedure, a descriptive quantitative analysis demonstrated that 47% of the participants were categorized as having a positive response toward telemental health, 32% as having a negative response, and 21% as being neutral or undecided. CONCLUSIONS: Valuing Indigenous knowledge can help us understand community members' experiences of and concerns with telemental health and inform more successful and appropriate initiatives. With the invaluable support of the KO Telemedicine co-authors, we offer ways forward to address concerns identified by the community members. Most importantly, any ways forward for community telemental health initiatives need to be community driven and community led.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".