Identification and classification of childhood developmental difficulties in the context of attachment relationships.
Bibliographic record
Abstract
OBJECTIVE: This paper addresses challenges in identification and classification of childhood difficulties in the context of the current psychological literature on early attachment relations and normative development. METHOD: A review of the literature on childhood development and attachment relationships was conducted in relation to recent advances in developmental psychology. RESULTS: Findings include recommendations for studying the child in ecological context, focusing on positive assets and resiliency, and seeing children as active participants in the construction of their own environmental niches. Studying the active strong child in context involves taking an integrative view by investigating the interactions of all basic biopsychosocial facets of the child's world, recognizing the delicate balance between pathologizing and insisting that all behaviour and psychological states are equally valid expressions of a normative developmental course. Further, developmental science now has amassed the requisite data to establish the need for taking attachment relationships into careful account in assessing a child or youth's biopsychosocial wellbeing. CONCLUSIONS: It is thus argued here that identification of children in psychological distress requires an holistic, contextually inclusive, examination of their early and subsequent attachment experiences and positive relations if a diagnosis is to lead to appropriate, efficacious, intervention.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".