Beyond traditional notions of validity: Selecting appropriate measures for occupational therapy practice
Bibliographic record
Abstract
Increasingly, tied to factors such as increased emphasis on accountability and health care spending restraints, occupational therapists are required to implement evidence-based practice. When implementing evidence-based assessment, it is critical that occupational therapists examine and challenge some of the assumptions underlying the current use of measures, as well as the conclusions being drawn from their use. In order to most effectively judge the appropriateness of measures informing their assessments, interventions and recommendations, occupational therapists must draw upon measurement theory and concepts. To date, occupational therapy literature has predominantly emphasized a narrow conceptualization of validity that focuses on empirical evidence. The work of Samuel Messick provides a more comprehensive framework of construct validity that considers both empirical and ethical issues. This framework binds the social consequences of assessment to the evidential basis of test interpretation and use, and challenges occupational therapists to broaden their consideration of validity by addressing both ethical and empirical issues when choosing measures.
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 | 0.270 | 0.635 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".