Experiences of psychiatrists and support staff providing telemental health services to Indigenous peoples of Northern Quebec
Bibliographic record
Abstract
Abstract Background Due to regional, professional, and resource limitations, access to mental health care for Canada’s Indigenous peoples can be difficult. Telemental health (TMH) offers the opportunity to provide care across vast distances and has been proven to be as effective as face-to-face services. To our knowledge, there has been no qualitative study exploring the experiences of TMH staff serving the Indigenous peoples in Northern Quebec, Canada; which is the purpose of this study. Methods Using a qualitative descriptive design, the entire staff of a TMH clinic was recruited, comprising of four psychiatrists and four support staff. Individual semi-structured interviews were conducted through videoconferencing, and results were thematically analyzed. Results To address the mental health gap in Northern communities, all psychiatrists believe in the necessity of in-person care and note the synergistic effect of combining in-person care and TMH services. This approach to care allows psychiatrists to maintain both an insider and outsider identity. However, if a patient’s condition requires hospitalization, then the TMH staff face a new set of information sharing and communication challenges with the inpatient staff. TMH staff believe that the provision of culturally sensitive care to Northern patients at the inpatient unit is progressing; however, more work needs to be done. Despite the strong collegial atmosphere within the clinic and collective efforts to provide quality TMH services, all participants express a sense of frustration with the paper-based and scattered documentation system. Conclusion The TMH team works in cohesion to offer TMH services to Indigenous peoples; yet, automatization is needed to improve the workflow efficiency within the clinic and collaboration with the Northern clinics. More research is needed on the functioning of TMH teams and the separate but important roles of each team member.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".