Impact of Multigenerational Interactions on Travel Experience and Well-being of Elderly Tourist Insight from the Tourism Industry
Bibliographic record
Abstract
Prior research on elderly travel has mostly examined the travel goals, situations, and happiness of older individuals as distinct factors. However, there has been little investigation into the influence of multigenerational interactions on the journeys and well-being of elderly tourists. Recognizing the urgency and importance of this gap, this study explores the impact of interpersonal connections with younger generations on the travel experiences and well-being of older individuals. Extensive research involving elderly travelers reveals that their interactions with their adult children often occur exclusively during shared travel, encompassing the period before, during, and after the trip. The findings also indicate that the recognition of elderly travelers’ experiences by their adult offspring significantly influences the quality of these relationships, which in turn affects the well-being of the elderly. Therefore, conducting a thorough examination of how intergenerational interactions impact the travel experiences and happiness of older individuals enables the tourism industry to make necessary adjustments to its products and services. This approach not only enhances intergenerational connections but also contributes to the growth of the elderly tourist market and boosts the profitability of destinations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".