A multi-method approach to studying activity setting participation: integrating standardized questionnaires, qualitative methods and physiological measures
Bibliographic record
Abstract
Purpose: This study investigated the feasibility of integrating three diverse methods for studying disabled youth’s experiences of activity settings: standardized questionnaires, qualitative methods and physiological measures. The article describes a case study of data collection in a real-life setting to outline the opportunities, challenges and lessons learned for future research. Methods: The methods included (a) two newly developed quantitative measures of qualities of home and community activity settings (MEQAS) and youth experiences (SEAS); (b) youth-friendly qualitative methods, including photo-elicitation, observations, electronic interviews and face-to-face interviews; and (c) an innovative system to collect physiological data (the HEART system) that provided insight into the experiences of youth beyond other methods. Results: The study demonstrated that these diverse methods measures can be practically combined to study activity setting participation experiences. The measures provided different types of data that informed one other and allowed a rich interpretation and deep understanding of the participant’s experiences. We discuss practical lessons concerning the joint use of the three methods, lessons specific to each method, and lessons concerning next steps for integrating the data. Conclusions: Combined methodologies hold great promise for investigations of the participation experiences of disabled youth in future research. Implications for RehabilitationThis feasibility study demonstrated that standardized questionnaires, qualitative methods, and physiological measures can be practically combined to study the real-world activity setting participation experiences of disabled youth.Combined methodologies hold great promise for investigations of the participation experiences of “hard to study” disabled youth in future research.Clinically it is important to look at youth-based experiences and priorities when planning interventions rather than focusing on specific skills or inclusion for its own sake.
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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 | 0.162 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| 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".