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Record W4306843911 · doi:10.3389/fpubh.2022.997981

Caring for the caregiver: Why policy must shift from addressing needs to enabling caregivers to flourish

2022· article· en· W4306843911 on OpenAlexafffund
Brian Beach, Louise Bélanger-Hardy, Susana Concordo Harding, Mônica Rodrigues Perracini, Linda Garcia, Ishika Tripathi, Margaret Gillis, Briony Dow

Bibliographic record

VenueFrontiers in Public Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Ottawa
FundersNarodowa Agencja Wymiany AkademickiejUniversity of Ottawa
KeywordsContext (archaeology)Caregiver burdenWork (physics)Caregiver stressPsychologyNursingPublic relationsBusinessMedicinePolitical scienceDementia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.321
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2022
Admission routes2
Has abstractyes

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