To Determine the Needs of Mothering Handling Training for Family Caregivers and Children with Cerebral Palsy at Home based on Gross Motor Function Levels in the City of Arak
Bibliographic record
Abstract
To Determine the Needs of Mothering Handling Training for Family Caregivers and Children with Cerebral Palsy at Home based on Gross Motor Function Levels in the City of Arak Abstract: Background: Understanding the real needs of children with cerebral palsy and their families helps the therapists to provide adequate health care service for them. This study aimed to determine the needs of mothering handling training for family caregiving of children and youth with CP at home based on the level of gross motor function. Materials and Methods: This research was a descriptive, analytical and cross sectional study that was performed on 186 children 4-12 year olds with CP from the rehabilitation clinics in the city of Arak. They were selected by simple and purposeful sampling. Clinical tests were Gross Motor Measure Function Classification System Expanded & Revised (GMFCS E&R) to assess the severity of gross motor function lesions and canadian Occupational performance measure (COPM) to determine the needs. Data were analyzed by descriptive tests such as: statistical test and two-way ANOVA. Results: The most important need of mothering handling training was self care training specially toileting, feeding, eating and functional mobility related to children with CP in the level of V of GMFCS E&R (transported in a manual wheelchair). There were no significant differences in needs of mothering handling training in areas of sex and severity of gross motor function lesions (p>0.05). Conclusion: It seems that therapists should combine maternal handling trainings with other interventions in therapy programs, especially in the area of self- care. Keywords: Child with cerebral palsy, Gross motor function classification system, Mothering handling
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".