‘Participation’: a systematic review of language, definitions, and constructs used in intervention research with children with disabilities
Bibliographic record
Abstract
AIM: Improving participation of children with disabilities is a priority; however, the participation construct lacks clarity. This systematic review investigated how researchers defined 'participation' and the language used in participation intervention research. METHOD: Nine health and education databases were searched for intervention studies of children with disabilities that included a participation outcome. Quantitative data were extracted using a customized form, and participation text data were extracted verbatim. Themes were derived using a thematic coding approach. These participation themes were applied to the outcome measures used in the included studies to compare participation language with the methods used to quantify participation changes. RESULTS: Of the 2257 articles retrieved, 25 were included in this review. Five participation themes and nine subthemes were developed. Two themes, attendance and involvement, were directly related to the participation construct. Three additional themes described related concepts: preferences, activity competence, and sense of self. INTERPRETATION: Attendance and involvement seem to describe the essence of the participation concept. The related themes may provide important avenues to enhance participation outcomes. This review highlighted the need for researchers to define the construct under investigation clearly and select measures carefully, as measurement choice is the mechanism through which the concept is operationalized in research.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".