Cultural adaptation and validation in Italian of the Seated Postural Control Measure for Adults 2.0
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the cultural adaptation and validation in Italian of the Seated Postural Control Measure for Adults 2.0 (SPCMA 2.0). METHODS: The original scale was translated and culturally adapted from French to Italian using the "Translation and Cultural Adaptation of Patient Reported Outcomes Measures-Principles of Good Practice" guidelines. Its internal consistency and test-retest reliability were examined. Its concurrent validity was evaluated using Pearson correlation coefficients with the Italian version of the Wheelchair use Confidence Scale and Wheelchair Skills Test 4.2. RESULTS: Fifty-nine people were evaluated and re-evaluated after 48 h. Most of the items and subscale totals were stable in the 2 evaluations as they reported an intraclass correlation coefficient value of >0.77. The test-retest analysis of the dynamic evaluation was performed on the same patients 48 h apart. The analysis for construct validity showed statistically significant correlations with Wheelchair use Confidence Scale and Wheelchair Skills Test 4.2. CONCLUSIONS: Seated Postural Control Measure for Adults 2.0 is one of the few tools that allow researchers to perform a quantitative and standardized posture assessment in a cost-effective and time-saving way. Furthermore, it has been demonstrated that it is an easy-to-administer scale and requires readily available tools. The limitations of this study highlighted above and the need to use quantitative and qualitative tools in clinical practice imply the need to conduct future studies.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".