The importance of feasible outcome evaluations: Developing stakeholder‐informed outcomes in a randomized controlled trial for children’s respite workers receiving pain training
Bibliographic record
Abstract
Abstract Objective Pain is common for children with intellectual and developmental disabilities. It is critical that caregivers have adequate pain assessment and management knowledge. The Let’s Talk About Pain program has shown promise to provide pain‐related knowledge and skills to respite workers; however, more systematic evaluation of the program is needed. This study aims to support Let’s Talk About Pain’s RCT development by using stakeholder input to help determine a feasible approach for collecting behaviorally based outcomes. A secondary aim is to discuss relevant considerations and implications for others in the disability field conducting similar work. Methods/Design Four employees in children’s respite organizations completed telephone interviews lasting approximately fifteen minutes and a questionnaire about feasible data collection approaches. Results The use of questionnaire and focus group methodology was determined to be the most feasible method to evaluate participants’ pain‐related approaches in practice. Conclusions Special consideration should be made when making methodological‐related choices during study development to help ensure study feasibility. The iterative approach described in this paper may also be helpful in clinical settings when designing program evaluations to enhance feasibility and suitability; it is particularly important for multifaceted organizations supporting individuals with complex needs including those with intellectual and developmental disabilities.
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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 | 0.405 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".