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Record W2041608943 · doi:10.1177/030802260006300804

The Impact of Community Paediatric Occupational Therapy on Children with Disabilities and Their Carers

2000· article· en· W2041608943 on OpenAlexaboutno aff
Sandra Stewart, Janet Neyerlin-Beale

Bibliographic record

VenueBritish Journal of Occupational Therapy · 2000
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyMedicineIndependence (probability theory)Activities of daily livingIndependent livingCommunity serviceGerontologyPhysical therapy

Abstract

fetched live from OpenAlex

The need to demonstrate ‘best value’ in local authority settings is one of the remits of the white paper Modernising Social Services (Department of Health 1997). This study investigated the effectiveness of paediatric occupational therapy in terms of both increasing the independence of disabled children and reducing the strain of caring on the part of the carers. A before-and-after design was used, in which 33 children with a wide range of disabilities were assessed using two measures — the Community Dependency Index and the Canadian Occupational Performance Measure — prior to receiving and then following occupational therapy. The carers were interviewed separately, using three scales to assess the strain associated with their caring tasks. The findings show a statistically significant shift in the children's scores indicating that the levels of independence improved for most of the children, although many remained dependent on their carers for a number of daily living tasks. While the carers found the occupational therapy service very satisfactory, the evidence suggests that it did not reduce the strain of caring.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.160
GPT teacher head0.466
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2000
Admission routes1
Has abstractyes

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