Identifying Generational Gaps in Emotional Expression among Youth and Parents
Bibliographic record
Abstract
Objective: This study aimed to identify and interpret generational differences in emotional expression between youth and parents, focusing on how social, cognitive, and cultural factors influence emotional communication patterns across generations. Methods and Materials: This qualitative research employed an exploratory design grounded in the interpretivist paradigm to capture the lived experiences of emotional communication within families. The study involved 20 participants from Canada, comprising 10 youth (aged 18–25) and 10 parents (aged 40–60), selected through purposive sampling to ensure diversity in gender, culture, and socioeconomic background. Semi-structured interviews were conducted in person and online, each lasting 45–70 minutes, until theoretical saturation was reached. Data were transcribed verbatim and analyzed thematically using NVivo 14 software. Thematic analysis followed Braun and Clarke’s six-step framework, with codes and categories refined through constant comparison and reflexive journaling to ensure credibility and consistency. Findings: Five major themes emerged: (1) Communication Styles Across Generations, reflecting youth’s verbal openness versus parental restraint; (2) Technological Mediation of Emotion, highlighting digital communication as both a bridge and barrier; (3) Emotional Norms and Cultural Expectations, emphasizing the impact of cultural scripts and gendered expression; (4) Perceptions of Empathy and Understanding, revealing emotional misattunement and validation gaps; and (5) Adaptation and Emotional Bridging Mechanisms, showing efforts toward intergenerational empathy and emotional literacy. The findings indicated that differences in emotional vocabulary, digital fluency, and cultural norms contribute to misinterpretations and emotional distance, while reflective dialogue and hybrid communication foster improved understanding. Conclusion: Generational gaps in emotional expression arise from interwoven social, cultural, and cognitive mechanisms that shape emotion socialization within families. Promoting emotional literacy, reciprocal empathy, and adaptive communication practices can enhance intergenerational cohesion and emotional well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".