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Record W4407266586

Family centered goalsetting and evaluation: Using standardized and individualized instruments

2012· article· en· W4407266586 on OpenAlexaboutno aff
Bjørg Fallang, Sigrid Østensjø, Ingvil Øien

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2012
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsStandardized testPsychologyMedical physicsMedicineMathematics education
DOInot available

Abstract

fetched live from OpenAlex

Purpose: Exploring how function and activities in individualized and standardized measures are related to family-selected goals, and compare change scores between the instruments. Design and Methods: A quantitative study of individualized and standardized instruments. Materials: Thirteen children with CP, mean age two years eight months participating in family-centered rehabilitation program. Canadian Occupational Performance Measure (COPM), Goal Attainment Scaling (GAS), Pediatric Evaluation of Disability Inventory (PEDI) and Gross Motor Function Measure (GMFM-66). Result: Parents identified 53 problems in COPM and 74 GAS-goals mainly in the categories personal care, mobility and play. Fourty-five percent of family-selected GAS-goals corresponded with activityproblems in COPM and skills in PEDI, while 26 percent of goals corresponded with function in GMFM-66. Individualized and standardized measures identified clinical change (p>.002), but did not correlate. Correlation between frequency of attained goals and improved scores varied in the individual child. Conclusion: COPM and PEDI are to a larger degree than GMFM reflected in family-selected goals. COPM may ensure familycentered practice, by facilitating the familys identification of activityproblems. PEDI and GMFM secure opportunities for standardized evaluation over time. All measures were sensitive to change, and low correlations indicate that they incorporate different aspects of motor function.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.557
GPT teacher head0.662
Teacher spread0.106 · 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.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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