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Record W1973422729 · doi:10.3109/01942638.2014.899284

Ecosystemic Needs Assessment for Children with Developmental Coordination Disorder in Elementary School: Multiple Case Studies

2014· article· en· W1973422729 on OpenAlexaff
Emmanuelle Jasmin, Sylvie Tétreault, Jacques Joly

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2014
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversité LavalUniversité de Sherbrooke
Fundersnot available
KeywordsPsychologyRelevance (law)Intervention (counseling)Socioeconomic statusData collectionNeeds assessmentSpecial needsOccupational therapyDevelopmental psychologyService providerApplied psychologyMedical educationService (business)Medicine

Abstract

fetched live from OpenAlex

This study explored the needs of children with developmental coordination disorder (DCD) from an ecosystemic viewpoint as part of a theory-driven program evaluation process. A multiple case study needs assessment was conducted. Participants included ten children with DCD, their parents (n = 12), teachers (n = 9), and service providers (n = 6). Data collection involved semi-structured interviews, validated questionnaires, and a review of the children's records. The results support the relevance of using an ecosystemic model to assess the needs of children with DCD in their life and social contexts. More specifically, the results highlight the need to provide additional services at school, such as occupational therapy and special education, as well as information and training regarding DCD for parents and teachers. The results also point to the relevant variables to consider in an intervention program based on theory-driven evaluations. This study shows how employing an ecosystemic frame of reference provides a better understanding of the needs of children with DCD. Future research should document the ecosystemic profiles and evolution of the needs of children with DCD with a larger sample from diverse socioeconomic backgrounds using a longitudinal study design.

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)0.000
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 score0.019
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.341
Teacher spread0.315 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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