Capturing and Cultivating Change: Daily Variability in Parental Attributions and the Role of Mindfulness
Bibliographic record
Abstract
Maladaptive parental causal attributions for child misbehavior have been identified as one of the lead culprits that reduce parent engagement in and readiness for behavioral parent training programs. As a result, these attributions are often assessed during clinical intake for child mental health difficulties. Despite their importance in the clinical domain, little research has focused on their day-to-day fluctuations and potential targets for their change that can ultimately have implications for clinical practice with parents. The present study examined daily changes in parental causal attributions using a daily diary method, and the role of parent mindfulness on these daily fluctuations. Participants were Canadian parents ( N = 156; M age = 38.2 years, SD = 5.46, 84.6% mothers) with children aged 3 to 12 years old ( M age = 5.93 years, SD = 2.42; 50.0% girls). Parents completed an initial measure of mindfulness at day one, and 14 further daily assessments of parental attributions. Multilevel modeling was employed for data analysis. Findings showed that parental attributions fluctuated across days, regardless of whether parental mindfulness was high or low. However, higher levels of mindfulness were associated with less maladaptive parental attributions overall. These findings suggest that (a) point-in-time assessments of parental causal attributions for child behavior may not always reflect a complete and accurate portrayal of parents’ cognitions, and (b) mindfulness pre-training before standard care may be effective in reducing overall biased parental attributions in the clinical intake process.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".