Examination of the Perceived Efficacy and Goal Setting System (PEGS) With Children With Disabilities, Their Parents, and Teachers
Bibliographic record
Abstract
The Perceived Efficacy and Goal Setting System (PEGS) is an instrument and a process that enables children with disabilities to reflect on their ability to perform everyday occupations and to identify goals for occupational therapy intervention. In this study, 117 children with disabilities in grades 1-3 completed the PEGS with occupational therapists who work in school settings. Children from 6-9 years of age with a variety of disabilities were able to self-report perceptions of their effectiveness performing 24 activities that would be expected of them each day. Parents and teachers, who completed a parallel questionnaire, rated their abilities lower than the children did. The School Function Assessment, a measure of the amount and type of support required for school participation, had low correlations with the Parent and Teacher PEGS questionnaires and did not correlate with the Child PEGS. No differences in perceived efficacy were found for children across grades or gender; however, differences were found across types of disabilities. Children were able to use the perceived efficacy information to identify and prioritize goals for intervention and these goals remained stable 2 weeks later. Occupational therapists can use the PEGS within a client-centered practice to help the child set goals for therapy and to incorporate explicitly the perspectives of parents and teachers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".