TWO YEARS IN THE DEVELOPMENT OF A NEW INTERDISCIPLINARY PEDIATRIC CHRONIC PAIN PROGRAM: OPPORTUNITIES, INITIATIVES, AND CHALLENGES
Bibliographic record
Abstract
Abstract BACKGROUND “…The field of paediatric pain medicine has demonstrated the benefits of interdisciplinary collaboration more than any other endeavour” (Law, Palermo, & Walco, 2013). Recently, the Ontario Ministry of Health and Long-term Care announced the funding of specialty paediatric chronic pain programs in several children’s hospitals across the province of Ontario, including McMaster Children’s Hospital. The Pediatric Chronic Pain Program includes Physicians (Pediatricians, Psychiatrist, Anesthesiologist), Psychologists, Child Life Specialist, Registered Nurse, Nurse Practitioner, Occupational Therapist, Physiotherapist, Social Workers, Pharmacist, and a Clinical Manager. OBJECTIVES The purpose of this poster is to highlight new initiatives within our clinic, including the development of a pain education session for families, group treatments (e.g., a 5 week Rise Above Pain Group; a 5-week Parenting Group), and a research database (to allow for program evaluation integrated within our clinical work). DESIGN/METHODS Challenges in developing a new clinic/new programs and providing care to complex families (e.g., professional roles and competencies, diagnostic discrepancies) will be discussed. CONCLUSION Implications for program development in new and established clinics will be highlighted.
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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 | 0.023 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".