Importance and clarification of measurement properties in rehabilitation
Bibliographic record
Abstract
OBJECTIVE: The purpose of this paper was to critically review the concepts and types of measurement reliability, validity, and responsiveness, and to discuss their implications for rehabilitation research and high-quality clinical practice. METHOD: A critical literature review considering the strengths, limitations, and appropriate applications of measurement properties in rehabilitation was conducted. RESULTS AND DISCUSSION: Measurement quality is assessed using criteria such as reliability, validity, and responsiveness. Many published studies do not report these measurement properties, which are related, sometimes overlapping, and are frequently confused. This review paper clarifies the meanings of the concepts and types of reliability, validity, and responsiveness. It gives examples that are relevant for the field of rehabilitation. It discusses how the measurement properties interact with each other and influence the size of the effect and the power of studies. CONCLUSION: Measurements are essential in rehabilitation research and clinical evaluation. Measurement properties should be reported to allow readers to evaluate the quality of the results presented. The clarification of measurement properties provided in this paper may contribute towards standardizing definitions and improving the quality of rehabilitation research and clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.527 | 0.791 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".