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Record W2546086395 · doi:10.5206/eei.v26i1.7734

Structured Intervention as a Tool to Shift Views of Parent–Professional Partnerships: Impact on Attitudes Toward the IEP

2016· article· en· W2546086395 on OpenAlexvenueno aff
Mariana Mereoiu, Sara Abercrombie, Mary Murray

Bibliographic record

VenueExceptionality Education International · 2016
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsIndividualized Education ProgramSpecial educationIntervention (counseling)PsychologyMedical educationProfessional developmentReferralDiversity (politics)Value (mathematics)PedagogyResponse to interventionTest (biology)MedicineNursingSociology

Abstract

fetched live from OpenAlex

The Individualized Education Program (IEP) is the roadmap that helps educators and families drive the education of students with disabilities, improve outcomes, and fulfill each child’s potential. However, the IEP can be challenging due to the large number and diversity of stakeholders, dynamics and culture of collaboration, and the complex procedures guiding the referral, evaluation, and placement. This study describes changes in attitudes toward the IEP reported by special educators and parents participating in a statewide six-month collaborative training model. Pre- and post-test data analysis indicates an interaction effect on overall attitude toward the IEP, with parents’ ratings of the value of the IEP decreasing at the end of the training and teachers’ ratings increasing. Moreover, special educators’ significantly higher ratings of the value of team planning for the IEP indicate enduring pre- and post-intervention differences. These findings have implications for school districts and agencies providing professional development to improve collaboration in IEPs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.159
GPT teacher head0.510
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueExceptionality Education InternationalSame topicFamily and Disability Support ResearchFrench-language works237,207