Caring for Caregivers: Establishing Resilience through Social Capital
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".