Ideals of counseling practice: Therapeutic insights from an Indigenous first nations-controlled treatment program.
Bibliographic record
Abstract
Indigenous Canadians suffer disproportionately from mental health concerns tied to histories of colonization, including exposure to Indian Residential Schools. Previous research has indicated that preferred therapies for Indigenous populations fuse traditional cultural practices with mainstream treatment. The present study comprised 32 interviews conducted with Indigenous administrators, staff, and clients at a reserve-based addiction treatment center to identify community-driven and practical therapeutic solutions for remedying histories of coercive colonial assimilation. Thematic analysis of semi-structured interviews revealed that counselors tailored therapy through cultural preferences, including the use of nonverbal expression, culturally appropriate guidance, and alternative delivery formats. Additionally, they augmented mainstream therapeutic activities with Indigenous practices, including the integration of Indigenous concepts, traditional practices, and ceremonial activities. Collectively, this integration of familiar counseling approaches and Indigenous cultural practices in response to community priorities resulted in an innovative instance of therapeutic fusion that may be instructive for cultural adaptation efforts in mental health treatment for Indigenous populations and beyond. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".