Strategic Guidance and Technological Solutions for Human Resources Management to Sustain an Aging Workforce: Review of International Standards, Research, and Use Cases
Notice bibliographique
Résumé
BACKGROUND: New technologies offer opportunities to create a healthy, productive, and capable aging workforce. There is little research from an organizational perspective about how technology can help create a sustainable aging workforce. OBJECTIVE: This study aims to (1) explore how technological solutions in organizations can help create and maintain a healthy, productive, and capable aging workforce; and (2) provide recommendations and strategic guidance that benefit both the aging worker and the organization. METHODS: International standardization practices, ethical frameworks, collaborative research, and use cases are used to demonstrate how technological solutions can be translated into practice and formed the basis for the development of a set of recommendations to create and maintain a sustainable aging workforce. RESULTS: Organizations need to look at aging through different lenses to optimize an age-inclusive workforce rather than viewing it by chronological age alone. International standards in technology, human resources management, and aging societies can form part of the solution to improve aging workforces. Digitalization of workplaces, digital literacy, innovation, intergenerational collaboration, and knowledge management form important elements of the international standard on age-inclusive workforce. Using internationally agreed ethical frameworks that consider age bias when designing artificial intelligence-related products and services can help organizations in their approach. Age bias in artificial intelligence development in the workplace can be avoided through inclusive practices. No blockchain application was found yet to improve the aging workforce. Barriers to blockchain adoption include fear of layoffs, worker resistance and lack of blockchain competence, worldwide adoption, support, and funding. Integrating blockchain into the internet of things may allow for improved efficiencies, reduce cost, and resolve workforce capacity problems. Organizations could benefit from implementing or funding wearable technologies for their workers. Recent tools such as the Ageing@Work toolkit consisting of virtual user models and virtual workplace models allow for the adaptation of the work processes and the ergonomics of workplaces to the evolving needs of aging workers. Lastly, selected use cases that may contribute to sustaining an aging workforce are explored (eg, the Exposure-Documentation-System, wireless biomedical sensors, and digital voice notes). CONCLUSIONS: The synergy of international standardization and ethical framework tools with research can advance information and communication technology solutions in improving aging workforces. There appears to be a momentum that technological solutions to achieve an age-inclusive workforce will undoubtedly find a stronger place within the global context and is most likely to have increased acceptance of technological applications among aging workers as well as organizations and governments. International standardization, cross-country research, and learning from use cases play an important role to ensure practical, efficient, and ethical implementation of technological solutions to contribute to a sustainable aging workforce.
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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 | 0,034 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,014 | 0,014 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,006 | 0,008 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».