Exploring the effects of self-reflection practice on cognitive emotion regulation and resilience among mothers of premature neonates
Bibliographic record
Abstract
Background & Aim: Despite the well-known benefits of practicing self-reflection in educational settings, little is known regarding the effects of applying it in clinical settings. The objective of the current study was to investigate the effects of self-reflective practice on cognitive emotion regulation and resilience of mothers of preterm infants in the NICU. Methods & Materials: A total of 90 mothers whose preterm infants were admitted to NICU enrolled in the current non-randomized clinical trial study by convenience sampling (n=45 in each group). The data of the control group were gathered prior to the intervention group. Pre- and post-test data were gathered using the demographic questionnaire, the Cognitive Emotion Regulation Questionnaire, and the Conner and Davidson Resilience Scale. Self–reflection practice was designed and conducted based on Gibbs' reflective cycle for the intervention group, which applied a blended model (face-to-face and virtual). Statistical analysis was conducted by SPSS-25 and using the repeated measure ANOVA. Results: Using ANCOVA, the results indicated that the self-reflection practice was effective in improving cognitive emotion regulation (F=66.01, P≤0.001, Eta=0.60) and resilience (F=89.43, P≤0.001, Eta= 0.67) among mothers in the intervention group. Conclusion: Self-reflection practice was an effective intervention for improving mothers’ skills, helping them be more resilient, and assisting them in regulating their emotions. Further studies should support the current study findings in different clinical settings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".