Translation and preliminary validation of the Italian version of the Family Impact of Assistive Technology Scale for Augmentative and Alternative communication (FIATS-AAC.it)
Bibliographic record
Abstract
BACKGROUND: The Family Impact of Assistive Technology Scale for Augmentative and Alternative Communication (FIATS-AAC) is an emerging parent-reported outcome measure designed to detect the functional impact of augmentative and alternative communication (AAC) interventions on family systems. OBJECTIVE: The present contribution reports on the adaptation of the FIATS-AAC into the Italian language. METHOD: The original FIATS-AAC was first translated in Italian by following a standard linguistic validation protocol that employed a translation-back-translation technique. To assess its preliminary measurement properties empirically, the initial Italian FIATS-AAC was then administered by either phone or face-to-face encounters to 30 parents or primary caregivers of children with AAC needs who were aged four to 18 years. Parents completed the scale twice with a one-week interval. During the first administration, parents also completed the standardized Impact on Family Scale as a comparative measure to assess convergent validity. RESULTS: Overall, the interpretation of results from internal consistency, test-retest reliability, and convergent validity suggest that the Italian FIATS-AAC is a promising tool to assess child and family functioning in areas that may be impacted by the introduction of AAC interventions. CONCLUSIONS: Recommendations for further study include confirmation of its responsiveness to detect meaningful functional change following the introduction of AAC interventions and the utility of a shortened version.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".