Triage Decision-Making in Interdisciplinary Pediatric Chronic Pain Programs
Bibliographic record
Abstract
BACKGROUND: Interdisciplinary pediatric chronic pain programs are ideal treatment settings for youth with chronic pain who are complex from a biopsychosocial perspective. There is currently no evidence-based clinical decision support to guide nurses triaging patients to such programs, which increases the risk for haphazard triage decisions. AIMS: To explore and describe the decision-making practices of and contextual influences on nurses triaging patients to interdisciplinary pediatric chronic pain programs. DESIGN: A qualitative exploratory descriptive design. SETTINGS: Interdisciplinary Pediatric Chronic Pain Programs. PARTICIPANTS/SUBJECTS: In all, 12 nurses across 11 different interdisciplinary pediatric chronic pain programs participated in this study. METHODS: Individual, semi-structured interviews were conducted, transcribed verbatim, and analyzed using concurrent content analysis, guided by the Cognitive Continuum Theory and the Theoretical Domains Framework. RESULTS: Findings focused on the complexity of the pediatric chronic pain population and the leading role nurses play in triage without evidence-based guidance. Analysis generated three prominent themes: (1) nurse-led triage determinants; (2) process of triage decision-making; and (3) external influences on triage decision-making. CONCLUSIONS: Triage decision making in the setting of interdisciplinary pediatric chronic pain programs is complex and often led by nurses. There is a desire amongst nurses to adopt an evidence-based clinical decision support triage tool (CDS), which may streamline the referral and triage process and foster a system whereby patients in highest need for interdisciplinary care are best prioritized.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".