ICF Linking and Cognitive Interviewing Are Complementary Methods for Optimizing Content Validity of Outcome Measures: An Integrated Methods Review
Bibliographic record
Abstract
Content validity is a fundamental requirement of outcome measures. After reviewing operational needs and existing definitions, content validity we as defined as: the extent to which a measure provides a comprehensive and true assessment of the key relevant elements of a specified construct or attribute across a defined range, clearly and equitably for a stated target audience and context. ICF linkage rules from 2002, 2005, and 2019 have provide increasingly clear processes for describing and evaluating content of outcome measures. ICF Core Sets provide international reference standards of the core constructs of importance for different health conditions. Both are important as reference standards during content validation. To summarize their use as reference standards, the following summary indicators were proposed: (1) Measure to ICF linkage, (2) Measure to (Brief or Comprehensive) Core Set Absolute Linkage, (3) Measure to (Brief or Comprehensive) Core Set Unique Linkage, (4) Core Set Representation, and (5) Core Set Unique Disability Representation. Methods to assess how respondents engage with content are needed to complement ICF-linking. Cognitive interviewing is an ideal method since it used to explore how respondents interpret and calibrate response to individual items on an outcome measure. We proposed a framework for classifying these responses: Clarity/Comprehension, Relevance, Inadequate response definition, Reference Point, Perspective modification, and Calibration Across Items. Our analysis of 24 manuscripts that used ICF linking for content validation since updated linking rules were published found that authors typically used linking to validate existing measures, involved multiple raters, used 2005 linking rules, summarized content at a concept level (e.g., impairment, activity, participation) and/or use core sets as a reference standard. Infrequently, ICF linking was used to create item pools/conceptual frameworks for new measures, applied the full scope of the 2019 linking rules, used summary indicators, or integrated ICF-linking with qualitative methods like cognitive interviews. We conclude that ICF linkage is a powerful tool for content validity during development or validation of PROM. Best practices include use of updated ICF linking rules, triangulation of ICF linking with participant assessments of clarity and relevance preferably obtained using cognitive interview methods, and application of defined summary indicators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".