Exploring the Arena of Work Disability Prevention Model for Stay at Work Factors Among Industrial Workers: A Scoping Review
Notice bibliographique
Résumé
Abstract Objective The aging workforce influences employability and health of the working population, with new challenges emerging. The focus has shifted from return to work only, to enhancing ability to stay at work. It is unclear whether factors that influence return to work (RTW) also apply to preserving health and helping workers stay at work (SAW). Study objectives were to identify factors contributing to SAW among industrial workers and map identified factors to the Arena of Work Disability Prevention model (WDP-Arena, a commonly used RTW model) to identify agreements and differences. Methods Scoping review; eight databases were searched between January 2005- January 2020. Manuscripts with SAW as outcome were included; manuscripts with (early) retirement as outcome were excluded. Factors contributing to SAW were mapped against the components of the WDP-Arena. Results Thirteen manuscripts were included. Most results aligned with the WDP-Arena. These were most often related to the Workplace and Personal system. Compared to RTW, in industrial workers fewer factors related to the Legislative and Insurance system or Health Care system were relevant for SAW. Societal/cultural/political context was not studied. Multidimensional factors (workability, vitality at work, balanced workstyle, general health, dietary habits) were related to SAW but did not align with components in the WDP-Arena. Conclusion Most factors that determine SAW in industrial workers could be mapped onto the WDP- Arena model. However, new influencing factors were found that could not be mapped because they are multidimensional. The life-course perspective in SAW is more evident than in RTW. Many elements of the Legislative and Insurance system and the Health Care system have not been studied.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,005 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».