The Effect of Leisure Constraints and Leisure Facilitators on "Working-after-retirement" Older Adults' Leisure Participation
Notice bibliographique
Résumé
Background: The aging population is becoming a global concern. Leisure can benefit older adults’ well-being, which can help older adults pursue successful aging. Older adults usually have more free time after their retirement. However, more and more older adults choose to work after retirement age. In Canada, older adults who are 60 years old or older can receive the retirement pension from the Canada Pension Plan (CPP), but one in five Canadian older adults aged over 65 years were still working in 2015 (Statistics Canada, 2017). This “working-after-retirement” (WAR) issue has not received attention within leisure studies. WAR is a complicated issue. It may bring both leisure constraints, such as lack of energy, and leisure facilitators, such as having coworkers as leisure companions. Objective: The purpose of my thesis was to investigate the relationship between leisure participation, leisure constraints, leisure facilitators, leisure motivation, and constraints negotiation among older adults who are experiencing WAR. Methods: An online survey was conducted to collect quantitative data regarding leisure participation, leisure constraints, leisure facilitators, constraints negotiation strategies, and leisure motivation from older adults in Canada who experienced WAR. The survey was distributed to Qualtrics’ panelists. A total of 417 participants completed the online survey. All participants were aged 55 years or older. After data cleaning, the final sample size was 396. Models were adopted from previous quantitative studies about leisure constraints and facilitators (Hubbard & Mannell, 2001; Son et al., 2008; Son et al., 2024). Partial least squares structural equation modeling (PLS-SEM) was applied to evaluate the validity of the measurement models and the structural model, examine the explanatory and predictive power of the models, and compare the models. Results: Eight modified constraints-negotiation models were tested in my thesis. The results indicated that leisure constraints were negatively related to leisure participation, while constraint negotiation and leisure motivation were positively related to leisure participation across all the models. I also found constraint negotiation partially mediated the paths between leisure motivation and leisure participation. The moderation effects of constraint negotiation and leisure constraints were not significant. All the models had weak explanatory power (.25 R2 < .50) based on Hair et al. (2011). The Mitigation and Moderation models had the highest PLS predictive power compared to linear regression model benchmark, while the Independence model was best among all the models in terms of BIC values. Discussion and Implications: My results supported the Independence and Mitigation models proposed by Hubbard and Mannell (2001). The Dual-channel and Facilitators models developed by Son et al. (2008) and Son et al. (2024) were supported as well. My results did not support the hypothesized moderation effect of constraint negotiation, which is in line with the lack of empirical support for the Buffer model (Hubbard & Mannell, 2001). In terms of model comparison, my findings suggested that the Independence model was the best model considering theoretical consistency and predictive power, although the Mitigation and Dual-channel models were favored in Hubbard and Mannell (2001) and Son et al. (2008), respectively. In terms of theoretical implications, my results favored the Independence model the most. The findings also supported my assumption that leisure facilitators play the role that parallels leisure constraints in the constraint negotiation process. With regard to practical implications, the most commonly reported leisure facilitators and negotiation strategies were intrapersonal facilitators (e.g., my leisure activities are enjoyable) and skill acquisition (e.g., I try to learn new leisure activities), respectively. Knowing these facilitators and negotiation strategies can encourage older adults to pursue leisure better. My study also suggested providing leisure education programs for WAR older adults may be a promising avenue to support rich leisure lives within this population.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».