Attachment Representations in a Sample of Neglected Preschool-Age Children
Bibliographic record
Abstract
A number of studies (see Éthier, 1999) have shown that neglect has a deleterious impact on children's development. However, the effect of neglect on a child's internal representations of their family still needs to be investigated. The aim of this study was to examine the attachment patterns observed in a subsample of neglected children as compared with a control group, using a representational assessment method. The overall sample consisted of 74 preschool-age children, with a clinical group comprised of 39 children referred to social services because of neglect and a control group including 35 children recruited in ordinary kindergartens. Children's attachment representations were assessed with the Doll Play Narrative Classification System (George and Solomon, 1990, 1996, 2000) as well as the complementary Disorganization Scales (George and Solomon, 1998). Mothers' reports consisted of: (1) an in-house socio-demographical questionnaire and (2) the shorter French version of the Parental Stress Index (Bigras et al., 1996). Results showed: (1) a significant difference in attachment representation classifications between the neglected group and the control group even when socio-economic status and maternal stress were controlled for and (2) a significantly higher proportion of avoidant attachment classification in the neglected group. Moreover, neglected avoidant children displayed more overall disorganized markers, and specifically more frightening markers, than normative avoidant children; they also depicted their mother as being less available than normative children. These results are discussed in the light of other findings as well as on theoretical grounds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".