The early retiree divests the workforce: A quantitative analysis of early retirement among health professionals
Notice bibliographique
Résumé
Introduction: Availability of health professionals is fundamental to a population's health. Despite shortages of health professionals, we know little about voluntary and involuntary exits from the workforce among publicly-employed Canadian Registered Nurses (RNs) and allied health professionals (AHPs). Limited data on "supply" inhibits the effectiveness of Canadianhealth human resource workforce planning. Early retirement is common among Canadian RNs; data are lacking on AHPs. Purpose: To determine whether publicly-employed Canadian RNs and AHPs differ in their approach to workforce departures between the ages of 45 and 85 years. Objectives: To: 1) develop and validate conceptual models of retirement among RNs and AHPs; 2) identify and compare factors reported to influence retirement decisions among RNs/AHPs; 3) explore the relative importance of factors on early vs. late/"on-time" retirement among RNs/AHPs; 4) quantitatively test conceptual models of early and involuntary retirement among RNs/AHPs; 5) evaluate, comparatively, model fit and association of identified variables with either early or involuntary retirement across occupational groups, and; 6) identify and discuss implications for RN and AHP workforce policy. Methods: To achieve objective 1, I reviewedthe retirement literature (n = 23 studies) and conducted interviews with Canadian RNs/AHPs (n = 14). My source of quantitative data, utilized to achieve objectives 2 through 6, was the Canadian Longitudinal Study on Aging (CLSA). To achieve objectives 2 and 3, I conducted exploratory data analyses (n = 794 RNs and n = 393 AHPs). To achieve objectives 4 and 5, Iconducted logistic regressions for the outcome of early retirement (n = 483 RNs and n = 177 AHPs). To achieve objectives 4 and 5, I conducted a logistic regression for the outcome of involuntary retirement using a combined RN and AHP sample (n = 277). Results: The conceptual model of early retirement had eight categories (38 variables): workplace characteristics; sociodemographics; attitudes/beliefs; broader context; organizational factors; family; lifestyle/health, and; work-related. The model of involuntary retirement had fourcategories (8 variables): broader context; sociodemographics; lifestyle/health and family. Caregiving responsibilities (variable) was added based on interview data. The average age of RN retirement (58.1 years) was significantly lower than that of AHPs (59.4 years). Financial possibility and desire to stop working were among the most frequently reported factors contributing to early and on time/"late" retirement among RNs and AHPs; 85% of RNs and 77% of AHPs retired early. The operationalized model of early retirement explained a maximum of 25% of variance in RN/AHP early retirement. Both RNs and AHPs whose retirement decision had been influenced by organizational restructuring were more likely to have retired early. RNs who felt retirement was financially possible and those with caregiving responsibilities were more likely to retire early. RNs noting a "desire to stop working" as a factor influencing retirement had lower odds of early retirement. Only 8% of variation in involuntary retirement was explained by the tested model. Only self-rated general health and occupation were associated with increased odds of involuntary retirement in a combined sample of RNs and AHPs. Discussion: RNs/AHPs consider many factors when contemplating retirement; some are sensitive to intervention, which generates possibilities for extending RN/AHP work-lives. The prevalence of involuntary retirement among RNs (23%) aligns with national prevalence; only 7% of AHPs reported involuntary retirement. More research is needed to i) deepen our understanding of publicly employed RN/AHP pathways to early and involuntary retirement, and ii) understand the reasons for differences in RN and AHP pathways to retirement. Conclusion: There is much to learn about publicly-employed RN and AHP pathways to retirement. The models tested in this studyhad much greater explanatory power for early retirement than involuntary retirement (25 vs 8% explained variance) suggesting that much is unknown regarding determinants of involuntaryretirement. The conceptual models have only been partially tested – further quantitative testing is needed; such testing requires a larger sample of RNs and AHPs and the inclusion of work-related variables. Potential strategies to reduce the rate of early retirement may include: reducing the frequency of restructuring in healthcare and improving its' implementation; legislation to expand paid leave policies to those providing informal care, and; subsidization of caregiving support forwould-be caregivers wishing to remain in the workforce. Work-based interventions that improve self-rated health may reduce the rate of involuntary retirement.
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 | 0,014 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».