Caring for Caregivers: Establishing Resilience through Social Capital
Notice bibliographique
Résumé
Introduction: Even with international consensus that carers play a crucial role in supporting high-needs populations and contribute to formal health system sustainability, the academic and policy literature offers inconclusive evidence to guide how to support carers to ensure their resilience.Theory and Methods: This research used a convergent mixed model parallel design consisting of three main research phases. The theory of social capital was used to explore the extent to which combinations of interventions (self-directed community care, psychosocial supports, financial supports, skill development), may strengthen bonds between individuals (care recipients, carers, and front-line providers) and their communities (organizations, community agencies) thereby improving access to resources resulting in benefits for carers, families, and formal health systems. The use of social capital presents a novel conceptual advance in research on carer resilience, the relationship between networks and system sustainability, and the benefits that can derive from the multidimensional aspects of social capital.Results: The results suggest cultivating broader frameworks of support to identify, assess, and address the ‘caregiver problem’. The findings substantiate both the social capital and resilience literature by identifying the role that network ties can play in improving access to resources. The results indicate that heterogeneous, mainly bridging and linking ties, might be more effective than bonding ties in improving a carer's resilience. Additionally, improved access to personal resources (a common focus for current policy interventions), is necessary, but on its own insufficient.Discussion: There has been increased recognition for supporting people and their carers. Yet, this focus remains largely at the individual level, and carer burden and burnout continues to be assessed and measured solely as a by-product of the complex (mainly medical) needs of the care recipient.Conclusions: This research identified the important role that healthcare systems plays in supporting an informal networks access to resources (personal, social, and societal) and resilience.Lessons Learned: The findings stress that the ‘caregiver problem’ is a complex phenomenon that requires a larger policy framework extending beyond one-off initiatives that are arbitrarily implemented. There is evidence to suggest the importance of conceptually understanding care networks from a broader perspective, acknowledging that carer resilience may be an individual-level phenomenon but can be supported or hindered by broader social- and societal-level impacts.Limitations: The sample of carers were identified through a ‘gatekeeper’ approach. Those who responded are likely to be a politically active population who have a potentially unique (yet context-sensitive) perspective. As such, this research may have neglected to identify more isolated, marginalized carers who may have perceived different or additional factors as crucial to support their resilience.Suggestions for future research: For the first time in 2015, persons aged 65+ exceeded the number of persons 0-14 in Ontario, Canada. There is value in engaging in comparative work with regions who have already experienced a precipitous decline in social networks to identify lessons learned as it relates to engaging beyond traditional avenues of carers – to explore the role of networks of care.
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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 | 0,007 | 0,016 |
| 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 | 0,003 | 0,005 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,008 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».