Development of a generic fidelity measure for rehabilitation intervention research for children with physical disabilities
Bibliographic record
Abstract
AIM: To increase research rigour and create a plausible way to assess clinical effectiveness, it is necessary to measure the degree to which interventions are delivered as intended (fidelity). Generic fidelity measures enable evaluation of more than one intervention through observation of unique and general characteristics relevant across interventions. This study describes the first generic fidelity measure in paediatrics. METHOD: Items were constructed from multiple sources to create a general attributes domain and two paediatric cerebral palsy (CP) intervention-specific domains. After a pre-testing procedure, raters were trained and videos of clinical interventions were rated to test the measure's psychometric properties. RESULTS: The Paediatric Rehabilitation Observational measure of Fidelity (PROF) consisted of 30 items. Six raters were trained on the PROF and rated 25 videos. Internal consistency (α) and interrater reliability (IRR) for the frequency scale showed the following results: context therapy: α=0.71, IRR=0.75; child therapy: α=0.85, IRR=0.87; and general attributes; α=0.78, IRR=0.82. Quality scale scores across domains demonstrated internal consistency greater than 0.80 and interrater reliability of less than 0.40. Pearson's correlations (r=-0.71, p<0.001) and analyses of variance (p=0.01) validated that each intervention domain was an independent construct. INTERPRETATION: The PROF is reliable and valid for evaluating interventions used for children with CP. Future studies may use the measure's framework, general attributes domain, and procedures to test the psychometric properties of other interventions.
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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 | 0.068 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".