Assessing disability in older adults: the effects of asking questions with and without health attribution
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of using questions with and without health attribution on scores derived from a self-report disability instrument. METHODS: We administered the disability component of the Late Life Function and Disability Instrument to 75 community-dwelling older adults. Then, we administered the same 16 questions with attribution to specific health conditions. We used a series of analytic methods including weighted Kappa coefficient, Bowker's Test of Symmetry and Rasch analysis to assess the effects of attribution formats. RESULTS: A higher prevalence of disability was reported in the non-health attributed compared with the health attributed questions (t = 5.76; p < 0.001, 95% CI 3.8-7.8). Item analyses indicated that participants were significantly more likely to report disability on the non-health attributed version on 4 of the 16 questions. CONCLUSION: For community-dwelling older adults, the use of a non-health attribution format may be preferable in instruments designed to assess prevalence of disability from contributing factors other than just health.
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 | 0.065 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".