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Record W4327596662 · doi:10.1111/jppi.12456

Parents helping a child with disability learn at home during <scp>COVID</scp>‐19: Experiences from Slovenia and Canada

2023· article· en· W4327596662 on OpenAlexaffabout
Majda Schmidt, Mateja Šilc, Ivan Brown

Bibliographic record

VenueJournal of Policy and Practice in Intellectual Disabilities · 2023
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsBrock University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicWork (physics)PsychologyMedical educationLearning disabilitySpecial needsPoint (geometry)Developmental psychologyMedicinePsychiatryEngineering

Abstract

fetched live from OpenAlex

Abstract The COVID‐19 pandemic caused much disruption in many global sectors, including education where schools were closed in most countries and children had to learn remotely from home. This was particularly challenging for children with special learning needs and disabilities, often already somewhat marginalized, as they were more likely to be left behind and less able to adapt easily to remote online learning. This study inquired into the experiences of 67 parents from Slovenia and 15 parents from Canada who helped their children with special learning needs or disabilities learn remotely. Parents in both countries identified several specific advantages and disadvantages to learning remotely from home. The Slovenian children spent more hours per day at their lessons and attended more lessons than the Canadian children. Both samples of parents received some support from their schools, although the Slovenia parents perceived these as more satisfactory. This study provides a unique opportunity to study the effects of remote learning during a prolonged crisis situation, and it provides valuable lessons for how both families and school personnel can work to improve the potential educational experiences of students who are required to learn remotely. A 12‐point framework for planning for future emergencies is provided.

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)0.120
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.120
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.359
Teacher spread0.311 · 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.

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

Citations5
Published2023
Admission routes2
Has abstractyes

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