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Record W2343281146 · doi:10.1080/19411243.2016.1141083

An Occupation and Participation Approach to Reading Intervention (OPARI) part II: Pilot clinical application

2016· article· en· W2343281146 on OpenAlexaboutno aff
Lenin C. Grajo, Catherine Candler

Bibliographic record

VenueJournal of Occupational Therapy Schools & Early Intervention · 2016
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)Intervention (counseling)PsychologyOccupational therapyCognitionDevelopmental psychologyAdaptation (eye)Clinical psychology

Abstract

fetched live from OpenAlex

The Occupation and Participation Approach to Reading Intervention (OPARI) is an intervention approach for children with reading difficulties that emphasizes reading as an important occupation of children. Part I presented the theoretical basis of the OPARI. Part II describes a pilot clinical application of the OPARI. Guided by Schkade and Schultz’s Occupational Adaptation model (OA; 2003) and blended with principles from the Cognitive Orientation to Daily Occupational Performance treatment approach (CO-OP; Polatajko & Mandich, 2004) the essential elements of the OPARI were actualized in an 8-week intervention program. The program included small group and individual therapy sessions twice a week and therapist-teacher-parent collaborations. Five children participated and were evaluated using three standardized assessments of reading, the Canadian Occupational Performance Measure (COPM), and a descriptive measure of reading participation. Qualitative data from parent reading log journals, teacher communications, and classroom observations were also gathered. Results indicated nonsignificant increases in reading scores, significant increases in perceived performance and satisfaction with reading participation as measured by the COPM, and descriptive increases in children’s participation, mastery, and frequency in reading.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.224
GPT teacher head0.506
Teacher spread0.282 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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