Creating a safe space for First Nations youth to share their pain
Bibliographic record
Abstract
INTRODUCTION: Indigenous children and youth may be quiet about the way they express their pain and hurt which is in contrast to how health professionals are trained to assess it. OBJECTIVES: The aim was to understand how youth from 4 First Nation communities express pain using narratives and art-based methods to inform culturally appropriate assessment and treatment. METHODS: This qualitative investigation used a community-based participatory action methodology to recruit 42 youth between 8 and 17 years of age to share their perspectives of pain using ethnographic techniques including a Talking Circle followed by a painting workshop. Physical pain perspectives were prominent in circle conversations, but emotional pain, overlapping with physical, mental, and spiritual pain perspectives, was more evident through paintings. Art themes include causes of pain and coping strategies, providing a view into the pain and hurt youth may experience. Youth were more comfortable expressing emotional and mental pain through their artwork, not sharing verbally in conversation. RESULTS: Circle sessions and artwork data were themed using the Indigenous Medicine Wheel. Content of the circle conversations centered on physical pain, whereas paintings depicted mainly emotional pain (eg, crying or loneliness; 74% n = 31) with some overlap with physical pain (eg, injuries; 54%), mental pain (eg, coping strategies; 31%), and spiritual pain (eg, cultural symbols; 30%). Common threads included hiding pain, resilience, tribal consciousness, persistent pain, and loneliness. CONCLUSION: Once a safe space was created for First Nation youth, they provided a complex, culturally based understanding of the pain and coping experience from both an individual and community perspective. These engaging, culturally sensitive research methods provide direction for health providers regarding the importance of creating a safe space for young people to share their perspectives.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".