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Record W3195597930 · doi:10.1177/00084174211035627

Self-determined Occupational Performance Model for Children From Economically Disadvantaged Backgrounds

2021· article· en· W3195597930 on OpenAlexvenueno aff
Laura Bray, Gilson J. Capilouto

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsnot available
Fundersnot available
KeywordsHandwritingDisadvantagedSocioeconomic statusPsychologyPovertyIntervention (counseling)Occupational therapyStressorPerspective (graphical)CognitionDevelopmental psychologyApplied psychologyMedicineClinical psychologyComputer scienceEconomic growthPopulationEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

Background. Children from low-income backgrounds have a higher incidence of handwriting challenges due to the unique social and environmental stressors associated with poverty. Additionally, children from economically disadvantaged households are at risk for motor, cognitive, and social deficits, which further impact their handwriting performance. Purpose. The purpose of this paper is to propose a theoretical model that provides a holistic perspective for addressing the handwriting needs of children from low-socioeconomic backgrounds. Key Issues. The presented conceptual model is derived from the person–environment–occupation model for occupational performance and self-determination theory. These theories reciprocally complement and enhance each other, providing a foundation from which clinicians can guide evaluation and intervention. Implications. Through the use of the proposed model, evaluation and intervention focus on intrinsic motivation while considering the physical, social, and cultural impacts on a child's occupational performance. The provider connects with the child's basic psychological needs, thus improving handwriting outcomes and facilitating improved academic performance.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.997

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 studies0.0010.000
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 head0.094
GPT teacher head0.371
Teacher spread0.277 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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