The relative influence of cognitive and emotional impulsivity on emotional availability in mothers with problematic substance use
Bibliographic record
Abstract
Emotional availability (EA) reflects caregiving that is sensitive, non-intrusive, and non-hostile, and provides the child with structure to learn and safely explore their world. While variable, EA can be lower in mothers with problematic substance use compared to non-clinical samples. Despite strong support for a relation between impulsivity and problematic substance use (including as it relates to regulation of strong positive or negative emotion), its impact on EA has not been explored. This relationship was examined in a sample of 29 mothers who were attending substance use treatment. Video recordings of mother and child (under 4 years of age) playing were coded for EA and mothers completed a self-report questionnaire examining impulsivity. Given shared variance among types of impulsivity (cognitive, positive, and negative) and EA dimensions (sensitivity, structuring, non-intrusiveness, and non-hostility), canonical correlational analysis (CCA) was used to identify variables contributing most strongly to the relation between impulsivity and EA. A single function accounted for 56% of variance. Cognitive and negative emotional impulsivity contributed significantly to structuring and non-intrusiveness dimensions of EA. Positive emotional impulsivity made a smaller nonsignificant contribution but was noted to support EA sensitivity. Developing awareness of impulsivity and times that it may hinder or support EA may enhance the quality of parent-child relationship development, improve parenting confidence, and decrease risk for harsh or neglectful parenting.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".