“It’s More than Just Needing money”: The Value of Supporting Networks of Care
Bibliographic record
Abstract
It is well established in research, practice, and policy that unpaid caregivers (family and friends of people with care needs) experience stress in their role. Supports that have been put in place by policy planners and program developers to support caregivers may not be accessed by caregivers at all or may do little to reduce their stress. Accessing personal resources (education, finances), in addition to social resources (individual connections) and societal resources (community supports) are critical in fostering resilience in caregivers (helping them adapt to stress and adversity). Social capital theorists argue that creating connections at various levels can improve access to resources. This research, through qualitative interviews (n = 21), identifies the different levels of resources required to address the needs of caregivers. Our findings indicate that interventions that focus on access to personal-level resources (education, funding) are important, but are on their own insufficient. Of more importance were interventions that work to improve relationships between formal providers and families; access to interdisciplinary teams; cross-sectoral collaborations; and inter-organization relationships, highlighting that a system that works together is likely to improve caregivers' access to resources.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".