Professional education and clinical application of balance control and reactive balance training: a cross-sectional national survey of Polish healthcare professionals
Bibliographic record
Abstract
BACKGROUND: There is a gap in educational resources and knowledge translation for reactive balance training (RBT). This study aimed to examine current educational practices and training experiences related to balance control and RBT among Polish healthcare professionals. METHODS: A cross-sectional online survey was distributed among members of a Polish regulated healthcare profession. Participants were eligible if they were licensed healthcare professionals involved in treating clients with balance or mobility impairments. The questionnaire included 55 items across six sections, covering demographics, clinical practice, treatment approaches, and knowledge, attitudes, barriers, facilitators, and clinical use of RBT. Data were analyzed using descriptive statistics and chi-square tests for group comparisons. RESULTS: Of the 286 valid respondents, only 26.5% reported using RBT in clinical practice, while 54.2% either had not heard of it or did not use it. Non-users commonly reported first learning about RBT through the survey itself (44.0%). Conceptual confusion with other approaches such as Bobath concept and Proprioceptive Neuromuscular Facilitation was evident. Users demonstrated higher confidence, greater workplace support, and fewer safety concerns. All groups expressed strong interest in further training, particularly through hands-on workshops and instructional videos. CONCLUSION: RBT remains underused in Poland, partly due to limited formal education and conceptual misunderstandings. Addressing these gaps through structured, hands-on training and institutional support may enhance implementation. These findings highlight the need for targeted educational strategies and broader knowledge translation efforts in underrepresented regions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".