Mental health professionals' perspectives of telemental health with remote and rural First Nations communities
Bibliographic record
Abstract
We conducted an online survey and interviews amongst mental health workers in Canada who reported experience in working with rural and remote First Nations (although not necessarily telemental health). Sixty-three respondents (of the 164) to the online survey reported experience in working with clients in remote and rural First Nations. Only 16 of the online survey respondents with remote and rural First Nations experience reported having received training in videoconferencing use. When asked how frequently they used videoconferencing with clients, 51% reported never using it, 19% used it once every few months and 10% reported using it a few times a month. Approximately 50% of participants reported finding it useful. Approximately 38% found the technology easy or very easy to use, and 15% found it very difficult. Individual in-depth interviews were also conducted with professionals who had First Nations telemental health experience specifically (n = 5). A quantitative data analysis was used to explore their perceptions of usefulness and ease of use of telemental health, as well as the relationships among these constructs. Advantages, disadvantages and challenges in using the technology were identified from the qualitative data. Promising ways forward include incorporating traditional practices and the Seven Teachings into telemental health services.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".