Basic psychological need satisfaction and affect within the leisure sphere
Bibliographic record
Abstract
Research on leisure and well-being in non-Western contexts is rare. Our study addresses this issue by investigating whether satisfaction of three basic psychological needs – autonomy, competence and/or relatedness – influences four types of affective well-being – high-arousal positive (HAP) affect, low-arousal positive (LAP) affect, high-arousal negative (HAN) affect and/or low-arousal negative (LAN) affect – within the leisure sphere. Telephone survey data were collected from 583 Hong Kong Chinese employees. Structural equation modelling indicated that (1) autonomy, competence and relatedness need satisfaction were all significantly and positively correlated with HAP affect; (2) autonomy need satisfaction alone was significantly and positively related with LAP affect; and (3) autonomy need satisfaction alone was significantly and negatively associated with both LAN and HAN affects. Taken together, these results suggest that fulfilment of basic psychological needs, especially the need for autonomy, contributes to people’s overall affective well-being, within the leisure sphere. We discuss our findings in terms of two frameworks: basic psychological needs theory and the DRAMMA leisure model. We also explicate the practical implications of our study and provide future research recommendations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".