Clinical Strategies to Develop Connections, Promote Health and Address Pain From the Perspectives of Indigenous Youth, Elders, and Clinicians
Bibliographic record
Abstract
In this article we discuss findings from a community based, participatory action research study. The aim was to understand how Indigenous youth describe, experience, manage pain and hurt and how they seek care. A critical analysis guided by Two-Eyed Seeing and Medicine Wheel frameworks highlighted important clinical strategies for Indigenous youth to balance their health and reduce pain. This study is a partnership project with an Aboriginal Health Centre in Southern Ontario and the Canadian Institute of Health Research funded Aboriginal Children's Hurt and Healing Initiative (ACHH). The study gathered perspectives of Indigenous youth, Elders, and health clinicians using conversation sessions guided by a First Nations doctoral student and nurse researcher. Using the medicine wheel framework three main thematic areas emerged across the three groups and include (1) Predictors of Imbalance; (2) Indicators of Imbalance; and (3) Strategies to re-establish balance health in relation to pain. The main strategy includes considerations for clinicians using the acronym LISTEN (Language, Individual, Share, Teachable moments, Engage, and Navigate) approach that outlines strategies for clinicians that will be a safe guide to manage pain and hurt.
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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".