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Evaluating children's rehabilitation services: an application of a programme logic model

2004· article· en· W1991585680 on OpenAlexaffabout
Debra Stewart, Mary Law, Dianne J Russell, Steven Hanna

Bibliographic record

VenueChild Care Health and Development · 2004
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGoal Attainment ScalingRehabilitationService delivery frameworkLogic modelService (business)Service providerPerceptionMedical educationNursingPsychologyApplied psychologyMedicinePhysical therapyBusinessMarketing

Abstract

fetched live from OpenAlex

OBJECTIVES: To apply a programme logic model to evaluate the effectiveness of a new therapy service for children with special needs who were in transition from pre-school to kindergarten. SETTING: A children's outpatient rehabilitation centre in Ontario, Canada. MAIN OUTCOMES: The short-term outcomes included parents' perceptions of the transition process itself and the information they required, the children's skill development for the transition to kindergarten, and parents' perceptions of services and satisfaction with resources. METHODS: A combination of quantitative methods [Goal Attainment Scaling (GAS), Measure of Processes of Care (MPOC), Client Satisfaction Questionnaire (CSQ)] and qualitative interviews were used to evaluate both the process ('Outputs') and outcomes ('Short-term objectives') of the new therapy service. RESULTS: The children involved in the evaluation met or exceeded goals that were set by therapists and parents. Parents' perceptions of, and satisfaction with, the new service were higher than the provincial average. Qualitative data from interviews with parents and service providers supported the findings from standardized measures, and provided suggestions for future service delivery. CONCLUSIONS: The programme logic model provided researchers and service providers a collaborative and systematic approach to conducting programme evaluation in a relatively short-time frame. It appears to be a useful option for evaluation of other children's services.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.072
GPT teacher head0.427
Teacher spread0.355 · 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 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

Citations27
Published2004
Admission routes2
Has abstractyes

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