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Record W3186029031 · doi:10.15453/2168-6408.1839

Use of a Caregiver Coaching Model for Implementation of Intensive Motor Training for Hemiplegic Cerebral Palsy: A Case Study

2021· article· en· W3186029031 on OpenAlexaboutno aff
Sheryl Eckberg Zylstra, Aimee Sidhu

Bibliographic record

VenueThe Open Journal of Occupational Therapy · 2021
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingCerebral palsyOccupational therapyPsychological interventionPhysical medicine and rehabilitationConstraint-induced movement therapyMedicinePsychologyPhysical therapyRehabilitationNursingPsychotherapist

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.583
GPT teacher head0.545
Teacher spread0.038 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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