Using collaborative logic analysis evaluation to test the program theory of an intensive interdisciplinary pain treatment for youth with pain‐related disability
Bibliographic record
Abstract
Intensive interdisciplinary pain treatment (IIPT) involves multiple stakeholders. Mapping the program components to its anticipated outcomes (ie, its theory) can be difficult and requires stakeholder engagement. Evidence is lacking, however, on how best to engage them. Logic analysis, a theory-based evaluation, that tests the coherence of a program theory using scientific evidence and experiential knowledge may hold some promise. Its use is rare in pediatric pain interventions, and few methodological details are available. This article provides a description of a collaborative logic analysis methodology used to test the theoretical plausibility of an IIPT designed for youth with pain-related disability. A 3-step direct logic analysis process was used. A 13-member expert panel, composed of clinicians, teachers, managers, youth with pain-related disability, and their parents, were engaged in each step. First, a logic model was constructed through document analysis, expert panel surveys, and focus-group discussions. Then, a scoping review, focused on pediatric self-management, building self-efficacy, and fostering participation, helped create a conceptual framework. An examination of the logic model against the conceptual framework by the expert panel followed, and recommendations were formulated. Overall, the collaborative logic analysis process helped raiseawareness of clinicians' assumptions about the program causal mechanisms, identified program components most valued by youth and their parents, recognized the program features supported by scientific and experiential knowledge, detected gaps, and highlighted emerging trends. In addition to providing a consumer-focused program evaluation option, collaborative logic analysis methodology holds promise as a strategy to engage stakeholders and to translate pediatric pain rehabilitation evaluation research knowledge to key stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".