Depression and leisure-based meaning-making: anhedonia as a mediating factor
Bibliographic record
Abstract
Leisure experience has particularly positive impacts on people’s health and well-being when it is perceived as meaningful. Research also suggests that mental health conditions, such as depression, inhibit people from deriving meaningfulness from their leisure. However, it remains underexplored what in depression has this negative effect on leisure-based meaning-making. Anhedonia, one of the depression’s key symptoms that undermines one’s ability to experience enjoyment, may be an underlying mechanism. This is consistent with recent evidence that positive affect plays a significant role in experiencing meaning in life. The current study examined the relationship between depression, anhedonia, and leisure-based meaning-making. A total of 155 community-living individuals with depression participated in a cross-sectional online survey. Pearson’s correlation analysis suggested that leisure-based meaning-making was negatively associated with both depression and anhedonia. However, the following mediation analyses found that the relationship between depression and leisure-based meaning-making was fully mediated by anhedonia, making depression’s direct effect non-significant. Similar patterns were observed in sub-dimensions of leisure-based meaning-making: connection/belonging and identity. The findings suggest that the hedonic factor plays a role in leisure-based meaning making.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".