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Record W4319788413 · doi:10.1177/87568705221150514

Preparing Early Childhood Educators/Interventionists: Scoping Review Insights Into the Characteristics of Rural Practice

2023· article· en· W4319788413 on OpenAlexafffundabout
Silvia Vilches, Maria J. Pighini, Mary J. Stewart, Verena Rossa-Roccor, Beth McDaniel

Bibliographic record

VenueRural Special Education Quarterly · 2023
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsNorQuest CollegeUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsOutreachEarly childhood educationEarly childhoodEquity (law)Multidisciplinary approachPsychologyMedical educationPolitical sciencePedagogyMedicineSociologyDevelopmental psychologySocial science

Abstract

fetched live from OpenAlex

Rural regions struggle to retain early intervention (EI), special education (SE), and early childhood education (ECE) supports for children with developmental delay and/or disability, yet there is little guidance to prepare pre-service students for rural practice. This exploratory scoping review of rural EI/SE/ECE practice in the United States and Canada, where EI for children birth to 8 years is part of the education and development continuum (as opposed to health), found four characteristics: a broader scope of practice, the importance of being a whole person, doing more outreach to engage families, and negotiating personal/professional boundaries. Retention is enhanced when educators feel effective and appreciated. Regionalized (not national) funding sources may be limiting disciplinary advances, and cultural/racial inclusivity, equity issues, travel, and distance barriers were under-studied. Cross-national variation in EI/SE/ECE terms impeded the search. Future research should highlight the unique multidisciplinary and multijurisdictional nature of rural EI/SE/ECE practice across the developmental span.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.021
GPT teacher head0.387
Teacher spread0.366 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations6
Published2023
Admission routes3
Has abstractyes

Explore more

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