Leisure Involvement and Happiness Levels of Individuals Having Fitness Center Membership
Bibliographic record
Abstract
The aim of the study is to investigate the leisure involvement and happiness levels of the individuals who have any fitness center membership. Screening model was used in the research. The sample group of the research was composed of a total of 599 voluntary participants, 260 being “females” and 339 being “males”, who were using fitness centers and were selected using improbable purposeful sampling method. “Oxford Happiness Scale Short Form—OHS-F”, developed by Hills and Argyle (2002) and adapted into Turkish by Doğan and Cotok (2011) along with “Leisure Involvement Scale—LIS-F”, developed by Kyle et al. (2007) and adapted into Turkish by Gurbuz et al. (2018) were used in the study in addition to “Personal Information Form”. Descriptive statistical method (frequency, arithmetic mean, standard deviation) was used for the identification of the distribution of the participants’ information. In order to determine if the data had normal distribution or not, Shapiro-Wilk test for normality was conducted and in consequence of this test, t-test, single factorial MANOVA, ANOVA and Pearson Correlation tests were administered upon determining that the data were in accordance with parametric test conditions. According to the findings, while there was no significant difference found in the happiness levels in line with the gender, marital status and education levels of the participants; a significant difference was determined in the leisure involvement levels according to education level and gender, and yet no significant difference was found between the leisure involvement level and marital status. Besides, a positively significant relationship was determined between the level of happiness and leisure involvement. The restraints as well as the evaluations for future studies were discussed in this sense.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".