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Record W4386925973 · doi:10.1080/10901027.2023.2257147

Trauma-informed practice: A self-study examining the readiness of pre-service early years professionals

2023· article· en· W4386925973 on OpenAlexaff
Tina Bonnett, Elizabeth Gould, Courtney Gratton, Sumera Nawaz Malik, Jenna Zinck

Bibliographic record

VenueJournal of Early Childhood Teacher Education · 2023
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsFanshawe College
Fundersnot available
KeywordsPsychologyPreparednessGraduation (instrument)CurriculumMedical educationPedagogyEarly childhood educationSituatedService (business)Medicine

Abstract

fetched live from OpenAlex

Childhood psychological trauma is currently gleaning attention in many fields of study. Subsequently, Trauma-Informed Practice (TIP) is becoming integral to many organizations that service children and families. A gap, however, is noted in literature between this approach to practice and pre-service early years professionals who study to work in care and education settings as either early childhood educators or teachers. Furthermore TIP, using Self-Study (SS) methodology, is deficient in literature. Thus, a Self-Study was enacted by four pre-service early years professionals, in their fourth and final year of their degree, to investigate their preparedness to employ TIP in their work post-graduation. A Critical Friend, versed in this topic, engaged in this study as this is habitual to SS methodology. Findings of this research convey that TIP is covertly addressed in course content, indicating that this area of pedagogy requires more explicit attention in pre-service curriculum. Outcomes of this study also illuminate the urgency for early years professionals to address their own adverse experiences so that they are situated to cultivate attuned and responsive trauma-informed climates in their pedagogical practice.

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)0.000
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 score0.765
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.371
Teacher spread0.335 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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