Using Traditional Spirituality to Reduce Domestic Violence Within Aboriginal Communities
Bibliographic record
Abstract
OBJECTIVES: We report the results of involving traditional healing elders (THE) in the clinical care of aboriginal families who were involved in domestic violence in the context of a clinical case series of referrals made for domestic violence. METHODS: Psychiatric consultations were requested from senior author L.M.M. for 113 aboriginal individuals involved with domestic violence as recipients or perpetrators (or both) between July 2005 and October 2008. As part of their clinical care, all were encouraged to meet with a THE, with 69 agreeing to do so. The My Medical Outcomes Profile 2 scale was being used as a clinical instrument to document effectiveness. Elders used traditional cultural stories and aboriginal spirituality with individuals, couples, and families to transform the conditions underlying domestic violence. RESULTS: For those people who met with the THE, a statistically significant change (p < 0.0001) occurred in symptom severity from baseline to final interview of 4.6-1.52 on a scale of 0-6. The most common presenting symptom was being beaten (39 people), followed by drinking (37 people), drugs (13 people), grudges and anger (12 people), sadness (9 people), hates self (8 people), fear (7 people), sleep problems (6 people), anxiety (5 people), and lost spirituality (2 people). Each person chose two primary symptoms to rate. CONCLUSIONS: Including elders in the care of people who are the recipients of domestic violence is effective. We speculate that it helps by providing traditional stories about relationships and roles that do not include violence. Spiritual approaches within aboriginal communities may be more effective than more secular, clinical approaches. Research is indicated to compare elder-based interventions with conventional clinical care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".