MétaCan
Menu
Back to cohort
Record W4386538560 · doi:10.5206/eei.v33i1.16749

Evaluation of the Effects of Pyramid Model Training on the Attitudes and Practices of Early Childhood Educators

2023· article· en· W4386538560 on OpenAlexaffvenueabout
Alexandra Rothstein, Mélina Rivard

Bibliographic record

VenueExceptionality Education International · 2023
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsEarly childhoodPsychologyInclusion (mineral)Competence (human resources)Early childhood educationPerceptionMedical educationSession (web analytics)Developmental psychologyPedagogyMedicineSocial psychology

Abstract

fetched live from OpenAlex

There is growing evidence of the effectiveness of the pyramid model (PM) in promoting young children’s social-emotional competence and reducing challenging behaviours. In the province of Quebec (Canada), as in many other regions, many children with special needs are integrated into early childhood settings where educators have not had specific training in managing challenging behaviours. The current project’s objective was to evaluate, using a mixed-methods design, the effects of a two-day training session in PM practices provided to 33 educators working in inclusive early childhood settings in the province of Quebec. Before the session, educators had reported that inclusion is beneficial for the child but not for the early childhood educator, demonstrating a need for more training and resources to be provided to early childhood educators. Following the session, their perceptions of the usefulness and social validity of the training in PM were positive, and their implementation of PM practices significantly increased. Their perceptions of how inclusion affected them also became more positive; however, no differences were found in their overall attitudes when comparing results from pre- to post-test, indicating the need for further support.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.873
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.065
GPT teacher head0.384
Teacher spread0.319 · 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
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueExceptionality Education InternationalSame topicSoftware Engineering Techniques and PracticesFrench-language works237,207