Evaluating Caregiver Sensitivity to Infants: Measures Matter
Bibliographic record
Abstract
The significance of caregiver sensitivity for child development has been debated among scholars, not least due to sensitivity's inconsistent predictive value over time and across contexts. A lack of uniformity in the definition of sensitivity contributes to this debate, but shortfalls of intertool concordance and construct validity in the instruments used to assess sensitivity may also be at issue. This study examines correspondences among four established standardized measures of caregiver sensitivity in independent classifications of the same sample of mothers of infants. Fifty European American mother–infant dyads of diverse SES were independently assessed with three observational caregiver sensitivity measures: the Emotional Availability Scales (EAS; Biringen, 2008, Emotional availability (EA) scales manual (4th ed.): Part 1. Infancy/early childhood version (child aged 0–5 years). Colorado State University. Unpublished manuscript), the Parent Child Interaction—Nursing Child Assessment Satellite Training Feeding Scale (PCI‐NCAFS; Oxford & Findlay, NCAST caregiver/parent‐child interaction feeding manual, Seattle, WA: NCAST Programs, University of Washington, School of Nursing, 2015), and the Maternal Behavior Q‐Sort (MBQS; Moran, Pederson, & Bento, 2009, Maternal Behavior Q‐Sort (MBQS)–Overview, available materials and support. University of Western Ontario. Unpublished). Ratings were juxtaposed with classifications of the same sample based on the original Ainsworth Maternal Sensitivity Scales (AMSS; Ainsworth, 1969, Power, 6, 1379). The EAS, NCAFS, and MBQS are related to the AMSS, but large proportions of variance were unshared. Researchers and clinicians should be cautious when assuming that popular observational assessment instruments, commonly believed to measure a generic construct of caregiver sensitivity, are interchangeable, as these measures may evaluate different features of sensitivity to infants.
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 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 | 0.025 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".