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Epilogue and Future Directions

2019· book-chapter· en· W2994889966 on OpenAlexaff
Alice S. Carter, Rebecca DelCarmen‐Wiggins

Bibliographic record

VenueOxford University Press eBooks · 2019
Typebook-chapter
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsToddlerNormativePsychopathologyMental healthPsychologyVariety (cybernetics)TemperamentDevelopmental psychologyInfant mental healthDevelopmental psychopathologyClinical psychologyPsychiatrySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Abstract The second edition of the Oxford Handbook of Infant, Toddler, and Preschool Mental Health Assessment offers historical understandings and highlights recent enhancements in the assessment of infant, toddler, and preschool mental health. Authors present empirical findings across a variety of assessment methods and measures developed to capture variability in normative and psychopathological social-emotional development, including assessments of multiple dimensions of temperament, emotion regulation and dysregulation, behavior problems, social-emotional competencies, diagnostic symptoms, and diagnostic conditions. Notable in this edition is the consensus regarding the importance of identifying and assessing the relevant relational contexts in which early development is fostered. Also emphasized is the need for professionals to enhance their cultural awareness and responsiveness. Finally, greater dissemination of evidence-based young child mental health assessment methods is encouraged, which will require training interdisciplinary professionals in the cultural and domain expertise to identify and evaluate children with and at risk for clinically significant psychopathology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.215
Threshold uncertainty score0.718

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0060.007
Open science0.0020.003
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.2150.067

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 head0.020
GPT teacher head0.220
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2019
Admission routes1
Has abstractyes

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