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Record W2100018977 · doi:10.58464/2168-670x.1259

Enhancing Child Care for Children with Special Needs Through Technical Assistance

2014· article· en· W2100018977 on OpenAlexaboutno aff
Cyleste Collins, Rob Fischer, Nina Lalich

Bibliographic record

VenueJournal of Family Strengths · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingQuarter (Canadian coin)PsychologyPerspective (graphical)Special needsNursingMedicineMedical educationPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Children with special needs often require additional supports in child care settings. The provision of technical assistance (TA) and consultation to child care teachers is an established method for addressing this need. This study expands on existing research by bringing the perspective of different adults (parents, technical assistance consultants, teachers, and child care center directors) together to better understand the experiences of all parties involved in TA cases for children between the ages of three and five. The concerns most frequently leading to the consultation were social-emotional-behavioral (50.5%), developmental (32.3%), medical (28.3%), and environmental risk (14%), and one quarter of parents reported that their child had more than one of these concerns. Parents’ evaluations of the outcomes of the consultation were predicted by the parent’s race, level of education, and whether they saw a behavioral concern as the initial reason for the consultation. Open-ended comments provided more insight into each group of adults’ experiences, some of which included frustration about feeling involved/included in the consultation (for parents) and parents’ not being involved in and/or engaged with the consultation (for other adults). The study’s findings emphasize the importance of all adults working as a team to ensure the best possible care for children with special needs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.293
Teacher spread0.282 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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