Exploring validation of a graphic symbol questionnaire to measure participation experiences of youth in activity settings
Bibliographic record
Abstract
Participation has a subjective and private dimension, and so it is important to hear directly from youth about their experiences in various activity settings, the places where they “do things” and interact with others. To meet this need, our team developed the Self-Reported Experiences of Activity Settings (SEAS) measure, which demonstrated good-to-excellent measurement properties. To address the needs of youth who could benefit from graphic symbol support, the SEAS-PCSTM,1 was created. The purpose of this paper is to describe the development of SEAS-PCS and the preliminary study that explores the equivalency of the SEAS and SEAS-PCS. The SEAS and SEAS-PCS were compared in terms of the equivalency of meaning of stimulus items by 11 professionals and five adults who used augmentative and alternative communication, were familiar with PCS, and were fluent readers. Out of 22 items, 68% were rated as highly similar on a 5-point scale (M = 4.14; SD = .70; mdn = 4; range: 2.81–5.00). Subsequently, the 32% of the SEAS-PCS items that were rated below 4 were modified based on the participants’ specific comments. Further work is required to validate the SEAS-PCS. The next step could involve exploring the views of youth who use AAC.
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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 | 0.060 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 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".