Communication Within Families: Understanding Patterns and Impacts on Mental Health
Bibliographic record
Abstract
Objective: This review aims to explore the dynamics of communication within families and its impacts on mental health, highlighting the role of various communication patterns in shaping mental well-being. Methods and Materials: Employing a descriptive narrative review approach, this article synthesizes contemporary literature from databases such as PubMed, PsycINFO, and Web of Science, focusing on studies published between 2000 and 2023. The selection criteria targeted empirical studies examining the relationship between family communication patterns and mental health outcomes across diverse cultural contexts. Findings: The review identifies key communication patterns within families, including open, supportive, and conflictual communications, and their significant impacts on mental health. It reveals that positive communication patterns are associated with better mental health outcomes, while negative patterns correlate with adverse mental health effects. The findings also underscore the importance of cultural and contextual factors in understanding these relationships. Conclusion: Strengthening family communication patterns offers a critical pathway to enhancing mental health. Future research should focus on developing targeted interventions that foster positive family communication and address the nuanced needs of diverse populations.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".