Retirement Voluntariness: Meaning, Measurement and Predictors
Notice bibliographique
Résumé
The overall purpose of this doctoral thesis was to build a better understanding of the factors that contribute to involuntary retirement in Canada and elsewhere. The goal was to inform further research as well as policy and practice in prevention efforts. Three different studies were conducted, each guided by conceptual frameworks of retirement and the life course perspective. First, given the lack of clarity surrounding the concept of “retirement voluntariness”, the meaning and measurement of the term were examined. An array of terminology, definitions, and measures of retirement voluntariness were identified, which were categorized into two main conceptualizations: the perception of agency in the decision to retire; and the conditions that impact the decision to retire. Second, the literature was systematically scanned for factors explaining perceived involuntary retirement. While a limited number of articles were identified (n=9) some common factors were found such as disability and poor health status (n=7), younger age at retirement (n=5), and job loss (n=4). Third, explanatory variables were taken from those identified in the systematic literature and from existing theoretical models to develop a more comprehensive theoretical model of 54 predictors across seven domains. This model was then employed to conduct sex-disaggregated descriptive and multivariable logistic regression analysis using nationally representative longitudinal data from the Canadian Longitudinal Study on Aging (CLSA). Over one quarter (28%) of older workers who retired between baseline and follow-up one of the CLSA perceived their retirement to be involuntary. Among 37 possible predictors, across seven domains, 14 factors predicted perceived involuntary retirement in this population. Only four predictors were common to both women and men, i.e., retiring because of organizational restructuring, or disability, health, or stress for which odds were greater, and retiring because it was financially possible or because of wanting to stop working which had lower odds. Findings suggest the need for interventions to prevent work disability, financial education, and employment supports for those facing job loss. Further, interventions should be sensitive to the differences between women and men. Suggestions for further research as well as additional recommendations for the prevention of perceived involuntary retirement were offered.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,010 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».