The Kids Hurt App: Development and testing of a pain assessment tool for First Nations youth (Preprint)
Bibliographic record
Abstract
BACKGROUND First Nations children and youth may have unique ways to convey their health needs that have not been recognized by health providers. This may contribute to the disparity between high rates of mental health and physical pain and the low rates of treatment for the conditions they experience. Evidence suggests a colonial history has resulted in poor experiences with the healthcare system, lack of trust with health providers and miscommunication between clinicians and patients. Contemporary ways using both Indigenous and Western knowledge is needed to bridge the gap in communicating pain. OBJECTIVE The aim of this qualitative study was to test the usability of the Kids Hurt App with First Nations youth and clinicians working with youth. METHODS Using a Two-eyed seeing approach, a kids hurt icon based app was developed using concepts from validated mood and pain assessment apps combined with community-based research that gathered First Nations youth perspectives on quality, intensity and location of pain and hurt. The kids hurt app contains 12 screens accessible on a handheld mobile phone or tablet device. RESULTS Three rounds of low fidelity testing and two rounds of high-fidelity testing were conducted with 19 First Nations youth to determine the usability of the Kids Hurt app. High-fidelity testing was also conducted with 15 clinicians after completing the youth sessions. Youth had constructive suggestions that were used to improve the app in subsequent rounds of version testing. There was one main discrepancy between youth and clinicians related to in visual preference for way to convey pain. Youth’s preference was maintained in the app. CONCLUSIONS All youth in all rounds of testing indicated they would use the Kids Hurt app if it was available to them in a health care setting with most clinicians noting the app would be useful in practice. CLINICALTRIAL N/A
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".