Understanding the Impact of the Pain Experience on Aboriginal Children's Wellbeing: Viewing through a Two-Eyed Seeing Lens
Bibliographic record
Abstract
Pain is a universal experience all humans share but can be unique in how it is expressed. The pain experience is influenced by several dynamic factors, including family, community and culture. When it comes to pain expression children are among the most vulnerable often due to difficulty conveying their discomfort. Childhood pain can have significant physical and developmental effects that can last into adulthood. These negative health outcomes may be more pronounced in Aboriginal children given (a) the high prevalence of painful conditions, (b) potential cultural differences in pain expression, (c) the lack of culturally relevant reliable pain assessment approaches; (d) the subsequent shortcomings in pain care resulting in persistent pain (e) impact on wellbeing and untreated childhood pain. Standardized pain scales are based on Western ways of interpreting pain and may not capture the complexities of this experience through Indigenous understandings. Integration of both Western and Indigenous knowledge is accomplished when employing a Two-Eyed Seeing approach which utilizes the best of both Indigenous and Western knowledge. We want to establish reliable means for Aboriginal children to convey pain and hurt from a holistic perspective. By using a Two-Eyed Seeing lens to examine these issues, we hope to learn how to improve health care encounters, reduce hurt and enrich the wellbeing of Aboriginal children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".