Use of a Caregiver Coaching Model for Implementation of Intensive Motor Training for Hemiplegic Cerebral Palsy: A Case Study
Bibliographic record
Abstract
Background: There is extensive literature to support the efficacy of both pediatric constraint-induced movement therapy (pCIMT) and hand-arm bimanual intensive therapy (HABIT) for children with hemiplegic cerebral palsy. In addition, there is increasing evidence to support the training of caregivers (parents and other care providers) to carry out therapy interventions in the home. The Family Activity Adaptation Model (FAAM) presents guidelines for parent and caregiver coaching for intensive therapies used in occupational therapy practice. Method: In this descriptive case study the FAAM was used to frame caregiver training to answer the question: Is a coaching model, using a participant other than the primary caregiver, an effective intervention delivery method for intensive therapies for a 2-year-old with hemiplegic cerebral palsy? Outcome measures included the Canadian Occupational Performance Measure, Pediatric Motor Activity Log, Pediatric Evaluation of Disability Inventory, Goal Attainment Scaling, and grip strength and range of motion. Results: The results demonstrated that gains were made on six of seven outcome measures, including both child performance outcomes and parent satisfaction measures. Conclusion: Caregiver coaching using FAAM principles for intensive motor therapy intervention resulted in gains on a variety of outcome measures. Further research is needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".