Caring for the caregiver: Why policy must shift from addressing needs to enabling caregivers to flourish
Bibliographic record
Abstract
Policies supporting caregivers ("caregiver policies") are limited in the extent to which they meet the needs of those who care for others. Where policies do exist, they focus on relieving the burdens associated with caring or the needs of the person they care for, rather than consider the holistic needs of the caregiver that would enable them to flourish. We argue that the established approach to caregiver policies reflects a policy failure, requiring a reassessment of current practice related to caregiver support. Often, caregiver policies target the care recipient rather than the caregiver's needs. Through a consultative exercise, we identified five areas of need that existing caregiver policies touch upon. Yet current approaches remain piecemeal and inadequate in a global context. Caregiver policies should not just relieve burden to the extent that caregivers can continue in the role, but they should support caregivers to flourish, and future work may benefit from drawing on related frameworks from positive psychology, such as the PERMA™ model; this is important for both policymakers and researchers.
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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 | 0.060 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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".