ATTACHMENT AND CAREGIVER–INFANT INTERACTION: A REVIEW OF OBSERVATIONAL‐ASSESSMENT TOOLS
Bibliographic record
Abstract
The relationship between maternal-infant interaction and attachment quality to infant developmental outcomes has long been established. As children mature, problems stemming from troubled caregiver-infant relations may result in referral to mental health or child protection services. The accurate and appropriate assessment of attachment is critical for early recognition of problematic relations and for informing suitable treatment modalities. Evaluating the quality of attachment poses a challenge for researchers and clinicians seeking to explore the association between infant development and the quality of early caregiving experiences. Although providing a definitive answer to the question of which of these assessment procedures is the single universal standard for measuring attachment quantity is beyond the scope of this article, readers will be provided with a description and comparison of strengths and limitations of the most commonly used measures of attachment, including the Strange Situation Procedure (M.D.S. Ainsworth, M.C. Blehar, E. Waters, & S. Wall, 1978), Attachment Q-Sort (E. Waters & K.E. Deane, 1985), Toddler Attachment Sort (TAS-45; J. Kirkland, D. Bimler, A. Drawneek, M. McKim, & A. Scholmerich, 2004), CARE-Index (P. Crittenden, 1985), Atypical Maternal Behavior Instrument for Assessment and Classification (AMBIANCE; E. Bronfman, E. Parsons, & K. Lyons-Ruth, 1999), Massie-Campbell Scale of Mother-Infant Attachment Indicators During Stress Scale (Attachment During Stress Scale; H.N. Massie & B.K. Campbell, 1983), and the Risky Situation Procedure (D. Paquette & M. Bigras, 2010).
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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 | 0.031 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".