COVID-19 and the ageing workforce: global perspectives on needs and solutions across 15 countries
Notice bibliographique
Résumé
Abstract Background COVID-19 has a direct impact on the employment of older people. This adds to the challenge of ageism. The World Health Organization has started a worldwide campaign to combat ageism and has called for more research and evidence-based strategies that have the potential to be scaled up. This study specifically aims to identify solutions to combat the adverse effects of COVID-19 on the global ageing workforce. Methods We present 15 case studies from different countries and report on what those countries are doing or not doing to address the impact of COVID-19 on ageing workers. Results We provide examples of how COVID-19 influences older people's ability to work and stay healthy, and offer case studies of what governments, organizations or individuals can do to help ensure older people can obtain, maintain and, potentially, expand their current work. Case studies come from Australia, Austria, Canada, China, Germany, Israel, Japan, Nigeria, Romania, Singapore, Sweden, South Korea, Thailand, United Kingdom (UK), and the United States (US). Across the countries, the impact of COVID-19 on older workers is shown as widening inequalities. A particular challenge has arisen because of a large proportion of older people, often with limited education and working in the informal sector within rural areas, e.g. in Nigeria, Thailand and China. Remedies to the particular disadvantage experienced by older workers in the context of COVID are presented. These range from funding support to encouraging business continuity, innovative product and service developments, community action, new business models and localized, national and international actions. The case studies can be seen as frequently fitting within strategies that have been proven to work in reducing ageism within the workplace. They include policy and laws that have increased benefits to workers during lockdowns (most countries); educational activities such as coaching seniorpreneurship (e,g, Australia); intergenerational contact interventions such as younger Thai people who moved back to rural areas and sharing their digital knowledge with older people and where older people reciprocate by teaching the younger people farming knowledge. Conclusion Global sharing of this knowledge among international, national and local governments and organizations, businesses, policy makers and health and human resources experts will further understanding of the issues that are faced by older workers. This will facilitate the replication or scalability of solutions as called for in the WHO call to combat ageism in 2021. We suggest that policy makers, business owners, researchers and international organisations build on the case studies by investing in evidence-based strategies to create inclusive workplaces. Such action will thus help to challenge ageism, reduce inequity, improve business continuity and add to the quality of life of older workers.
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 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,002 | 0,015 |
| Intégrité de la recherche | 0,005 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
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 ».