Cross-Cultural Adaptation and Validation of the Brazilian Portuguese Version of an Observational Measure for Parent–Child Responsive Caregiving
Bibliographic record
Abstract
Responsive caregiving is the dimension of parenting most consistently related to later child functioning in both developing and developed countries. There is a growing need for efficient, psychometrically sound and culturally appropriate measurement of this construct. This study describes the cross-cultural validation in Brazil of the Responsive Interactions for Learning (RIFL-P) measure, requiring only eight minutes for assessment and coding. The cross-cultural adaptation used a recognized seven-step procedure. The adapted version was applied to a stratified sample of 153 Brazilian mother–child (18 months) dyads. Videos of mother–child interaction were coded using the RIFL-P and a longer gold standard parenting assessment. Mothers completed a survey on child stimulation (18 months) and child outcomes were measured at 24 months. Internal consistency (α = 0.94), inter-rater reliability (r = 0.83), and intra-rater reliability (r = 0.94) were all satisfactory to high. RIFL-P scores were significantly correlated with another measurement of parenting (r’s ranged from 0.32 to 0.47, p < 0.001), stimulation markers (r = 0.34, p < 0.01), and children’s cognition (r = 0.29, p < 0.001), language (r = 0.28, p < 0.001), and positive behavior (r = 0.17, p < 0.05). The Brazilian Portuguese version is a valid and reliable instrument for a brief assessment of responsive caregiving.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".