Notice bibliographique
Résumé
Globally, while the population is ageing, only 3% of older (≥65 years) adults are engaged in entrepreneurship. The Healthy Ageing agenda is responding to the population trends by increasing its focus on elements of active or productive ageing (incorporating older working) with quality of life as a primary outcome. Alignment and exploitation of the synergies between healthy ageing and older entrepreneurial engagement are lacking. Entrepreneurship education is broadening its focus and targeting inclusivity, albeit with as yet little focus on the older population. Older entrepreneurship, while tentatively linked with increased quality of life, has yet to receive direct attention in the healthy ageing literature, perhaps in part due to the lack of empirical evidence supporting healthy ageing outcomes directly associated with older entrepreneurial skills training or entrepreneurial endeavours. Limited data supports the engagement of people at or beyond retirement age in entrepreneurship and there is little understanding about this cohort’s needs and drivers for entrepreneurial skills training. This “Smart Ageing and Renewal through Entrepreneurial Skills Training” developed research programme provides ground-breaking exploratory work to address current knowledge gaps in entrepreneurial education for adults over 65 years. This study asks what concepts and individual facets have the potential to inform older healthy ageing aligned entrepreneurship as well as aligned bespoke older entrepreneurial skills training? A cross-disciplinary, conceptual alignment between the healthy ageing and older entrepreneurship domains was conducted, with twenty healthy ageing concepts identified as common to both literatures. The study then explores the identified aligned concepts in practice. Following an action research framework, this study first sought key stakeholder feedback on the suitability of existing processbased entrepreneurial education materials for older individuals. Suitable aligned measurement constructs for entrepreneurial skills training evaluation in the cohort were then identified and suitable measures for facets of older entrepreneurship were validated in an over 65s population. Aligned measures included: EuroQoL 5D-5L quality-of-life measure, CASP12 and ICECAP-O wellbeing measures, Montreal Cognitive Assessment (MoCA) and BRS6 resilience measure, total entrepreneurial competences, entrepreneurial activity level and entrepreneurial self-efficacy. The research programme next implemented practical testing of these aligned measures. This was achieved through sampling in the general older population (n=102) to establish existing cross-domain relationships between healthy ageing and older entrepreneurship measures and by a collaborative, inclusive feasibility pilot study of stakeholder-approved entrepreneurial skills training provision in a community setting. Promising relationships were identified between healthy ageing outcomes of quality of life, wellbeing and resilience and those of entrepreneurial competences and self-efficacy, with total entrepreneurial competence explaining 21% of the variance in the CASP12 wellbeing measure, (R2 change 0.21, Fchange (5,.90)= 8.03, p
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 | 0,005 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».