Psychometric properties of the Community Integration Questionnaire: a systematic review of five populations
Bibliographic record
Abstract
OBJECTIVES: This systematic review documents the content and the quality of the psychometric evidence concerning the utilization of the Community Integration Questionnaire for individuals living with a disability other than a traumatic brain injury. DATA SOURCES: Medline, Embase, CINAHL, OTseeker and PsycINFO (searched from inception to June 2019). REVIEW METHODS: Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used for conducting and reporting this review. Studies that investigated at least one psychometric property of the Community Integration Questionnaire for individuals living with a disability other than traumatic brain injury were included. Data extraction and critical methodological appraisal of the articles (MacDermid checklist, COnsensus-based Standards for the selection of health Measurement INstruments checklist) were independently performed and validated by the first two authors. RESULTS: = 0.71-0.84). Construct validity is fairly documented for adults living with multiple sclerosis or aphasia and in mixed samples. Test-retest reliably is acceptable for adults living with multiple sclerosis (intraclass correlation coefficient = 0.91-0.97) as well as responsiveness (area under the receiver operating characteristic curve = 0.81). Other psychometric properties could not be demonstrated sufficiently solid. CONCLUSION: Many psychometric properties of the Community Integration Questionnaire are still poorly evaluated for adults living with a disability other than a traumatic brain injury. However, promising data have been documented in each population included in this review.
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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 | 0.022 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".