The assessment for living with aphasia: Reliability and construct validity
Bibliographic record
Abstract
The Assessment for Living with Aphasia (ALA) is a pictographic, self-report measure of aphasia-related quality-of-life. Research was undertaken to assess test-re-test reliability, construct validity, and the ability to discriminate aphasia severity. The ALA was administered to 101 participants with aphasia on two occasions. Test-re-test reliability was evaluated using intra-class correlations and internal consistency using Cronbach's alpha. Three reference measures were administered to assess construct validity. A focus group reported on ease of administration and face validity. Analysis identified 15 out of 52 rated items for elimination. For the remaining items, test-re-test reliability was excellent for the total score (ICC = .86) and moderate-to-strong for a priori domains adapted from the WHO ICF (.68-.83). Internal consistency was acceptable-to-high. Significant correlations were observed between the ALA and reference tests (SAQOL-39, .72; p < .001; VASES, .62, p = .03; BOSS CAPD, -.69; p = .008). The language impairment domain discriminated between all aphasia severity groups, while mild aphasia was different from moderate and severe aphasia in participation and total scores. The ALA was reportedly easy to administer and captured key aspects of the experience of living with aphasia. Results suggest acceptable test-re-test reliability, internal consistency and construct validity of the ALA.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".