Bibliometric Analysis of Published Original Research Articles on Leisure Constraints between 1991 and 2019
Bibliographic record
Abstract
Background: The decrease in the frequency and duration of participation in leisure activities has negative effects on the physical, cognitive and psychological health of individuals. In this context, identifying the obstacles, which prevent individuals from participating in leisure activities, may offer important clues to institutions and organizations in taking measures to increase participation. Methods: The bibliometric analysis method was used in this study. The research was carried out with 306 articles scanned from the Web of Science (WoS) core database between 1991 and 2019. Results: Studies conducted in leisure area increased systematically according to 5-year periods and mostly authors from USA, Australia and Canada produced them. The mainstream subjects, which attracted the attention of researchers during recent years, are detected to be self-efficacy, segmentation, mental health and fear related to intrapersonal constraint topics. Conclusion: In order to cope with physical and mental health problems resulting from aging of the world population, and technological developments, and negative effects generated by inactive modern life style, priority should be given to studies on leisure constraints to be conducted on a more comprehensive basis. Another suggestion is to encourage health policies and applications which can increase participation in leisure activities.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.233 | 0.252 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".