Individual and contextual factors influencing children's effort in pediatric rehabilitation interventions
Bibliographic record
Abstract
Rehabilitation clinicians strive to encourage children's sustained effort within challenging practice conditions. Effort influences intervention success, yet it is rarely defined or measured. Effort can be conflated with individual factors, such as motivation and engagement, that might influence it. Contextual factors that likely impact children's effort, such as practice conditions and therapeutic interactions, are generally under-described. Defining, describing, and measuring effort and its influencers is necessary to enhance understanding of differences in rehabilitation intervention outcomes across individuals and contexts and to support the development of personalized precision rehabilitation approaches. This narrative review describes effort conceptualization in rehabilitation, particularly in relation to intensity, engagement, and participation nomenclature. The review outlines individual and contextual factors that may influence children's effort in rehabilitation and describes potential next steps for effort description and measurement. Subsequent work should aim to identify factors that can be targeted in clinical practice to promote and sustain children's effort in the rehabilitation process, thereby individualizing interventions and potentially improving their effectiveness. WHAT THIS PAPER ADDS: Effort as it relates to rehabilitation is confusingly described and infrequently measured. Engagement, involvement, intensity, and participation are terms alluding to effort. Child-specific and therapy-specific factors, alone and in combination, may influence children's effort. Clearer conceptualization of effort and the factors that influence it will support personalization of interventions. Better measurement will enhance knowledge about relationships between effort and therapeutic outcomes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".