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Record W4214541360 · doi:10.1111/1467-8578.12405

Research priority setting with parents of students with learning exceptionalities and disabilities

2022· article· en· W4214541360 on OpenAlexaboutno aff
Jennifer Baumbusch, Jennifer E. V. Lloyd

Bibliographic record

VenueBritish Journal of Special Education · 2022
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)PsychologyLearning disabilitySpecial educationMedical educationInclusion (mineral)PedagogyDevelopmental psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

With an increasing focus on knowledge mobilisation, there is a concomitant shift in how stakeholders, such as parents, ought to be engaged in the research process. The purpose of this study was to explore the research priorities of parents of Kindergarten to Grade 12 students with learning exceptionalities and disabilities in British Columbia, Canada. A cross‐sectional survey design was employed. A convenience sample of 130 parents completed the survey. From a list of pre‐selected variables, parents identified time to initial designation, high school graduation, and home‐schooling/independent schooling as the three most important research priorities, with learning disabilities, autism spectrum disorder, and moderate behaviour support/mental illness as the three most important designations. In an open‐ended response, parents recommended further areas for inquiry, including the effectiveness of inclusive education and school exclusion. Research priority setting can help create research agendas that align with the needs and interests of stakeholders.

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)0.052
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.060
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0100.002
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.045
GPT teacher head0.409
Teacher spread0.364 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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