Using Reasons for Living to Connect to American Indian Healing Traditions
Bibliographic record
Abstract
Responding to high rates of suicide for American Indian youth, helping professionals often struggle to connect healing traditions from American Indian cultures to tools from European psychology. The differences between American Indian healing and European therapy can be vast. Finding connections or building bridges between these two perspectives may be more difficult than it appears (Duran & Duran, 1995). One method to bring together these worldviews is to use the Reasons for Living Questionnaire (RFL, Linehan, Goldstein, Nielsen, & Chiles, 1983); the Reasons for Living Inventory for Adolescents (RFL-A, Osman, Downs, Kopper, Barios, Besett, Linehan, Baker, & Osman, 1998), or other psychological assessments developed using the RFL as a foundation. Reasons for Living (RFL) assessments have emerged as powerful strength based tools for assessing suicide risk (Range & Knott, 1997). RFL and RFL-A factors link to a relational worldview common to most American Indian people. A relational worldview considers a balance between forces often identified as spirit, context, mind, and body (Cross, 1998). Using RFL or RFL-A in suicide assessments allows practitioners to assess where youth may be out of balance in one or more of the four traditional areas: spirit, context, mind, and body. This may assist specific referrals to culturally appropriate healing. RFL and RFL-A assessments could be augmented to improve their correspondence to the relational worldview.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".