Translation and adaptation of the Gross Motor Function Measure-88 to the Croatian language
Bibliographic record
Abstract
The Gross Motor Function Measure-88 (GMFM-88) is a standardized observational instrument measuring gross motor function forchildren with cerebral palsy and Down syndrome. Clinicians in Croatia have been showing a growing interest in GMFM-88. However,only the English version of the measure was available, which possessed a barrier for its use in Croatia. The aim of this study was totranslate the GMFM-88 test from English into Croatian and adapt it for use in clinical practice in Croatia. In this study, we followedthe first five steps of the Sousa & Rojjanasrirat (2010) guideline for translation and adaptation. First, two authors independently did“forward” translation; a translation of the score sheet from English into Croatian. Then, the third author performed “backward” translation, which was checked and revised by the measure’s author. The pre-final version of the translated measure was tested throughcognitive debriefing with seven clinicians. Participants pointed out the importance of the consistent use of terms and provided suggestions for improvement, such as changed word order or shortening of the translated items. Seventy two of 88 items were changed.With this study, we have initiated the adaptation of the GMFM-88 to Croatian. The next steps for fully validated GMFM-88 in theCroatian language is to do psychometric testing with children with cerebral palsy and Down syndrome in Croatia.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".