Appraising the quality of tools used to record patient-reported outcomes in users of augmentative and alternative communication (AAC): a systematic review
Bibliographic record
Abstract
PURPOSE: People who have complex communication needs (CCN), and who use augmentative and alternative communication (AAC) to help them express themselves, can be difficult to engage in decision making about their healthcare. The purpose of this review was to identify what patient-reported outcome measures (PROMs) have been employed with people who use AAC. Of the tools identified, the review aimed to establish what conceptual frameworks were used and how the reports describe completion of the PROM. METHODS: A systematic literature review was carried out. A pre-defined set of search terms was entered into five main health and education databases. Titles and abstracts were sifted for relevance. Full text papers were screened against inclusion/exclusion criteria. Data pertaining to the type and nature of the PROM used was extracted. Complementary data sources were analysed to construct a narrative synthesis of the papers identified. RESULTS: Within 15 papers that met the review criteria, 25 PROMs were used with people who rely on AAC comprising of 15 separate measures. The conceptual frameworks for 12 of these tools were reported from which 62 items, or concepts being measured, were identified. Following synthesis of these items, 9 conceptual domains and 11 sub-domains were generated. Limited information was available about who completed the PROM nor how much, if any, support they received. CONCLUSIONS: No PROM that has been developed specifically for people who use AAC was identified by this review. Of the tools that have been used with people who use AAC, the concepts measured were broad and varied. The quality of reporting concerning who completed the PROM was limited, undermining the trustworthiness of many of the studies.
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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 | 0.100 | 0.397 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".