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Record W2090910743 · doi:10.1037/0012-1649.40.4.519

Attachment at Early School Age and Developmental Risk: Examining Family Contexts and Behavior Problems of Controlling-Caregiving, Controlling-Punitive, and Behaviorally Disorganized Children.

2004· article· en· W2090910743 on OpenAlexaff
Ellen Moss, Chantal Cyr, Karine Dubois‐Comtois

Bibliographic record

VenueDevelopmental Psychology · 2004
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPsychologyDevelopmental psychologySpouseStrange situationClinical psychologyAttachment theory

Abstract

fetched live from OpenAlex

Preschool to school-age trajectories of 242 children, including 37 with insecure-disorganized and 66 with insecure-organized attachment patterns, were examined. Child attachment and stressful life events (the latter retrospectively) were measured at ages 5-7, and mother-child interactive quality, parenting stress, marital satisfaction, and teacher-reported behavior problems were evaluated concurrently and 2 years earlier. Results indicated that all three disorganized subgroups had poorer mother-child interactive patterns and more difficult family climates than secure or insecure-organized children. The controlling-punitive group showed significant increases in maternal reports of child-related stress between preschool and school age. The controlling-caregiving group showed greater likelihood of loss of a close family member, and mothers of the insecure-other group reported lower marital satisfaction and greater likelihood of their own or a spouse's hospitalization. Controlling-punitive children had higher externalizing scores, and controlling-caregiving children higher internalizing scores, than secure children.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.333
Teacher spread0.306 · 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 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

Citations231
Published2004
Admission routes1
Has abstractyes

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