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Record W2186713180 · doi:10.12927/hcpap.2015.24397

Caring for the Family Caregiver: Lessons Learned in Child Health

2015· article· en· W2186713180 on OpenAlexaffvenueabout
Krista Keilty, Eyal Cohen

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2015
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsInstitute of Health Services and Policy ResearchHospital for Sick Children
Fundersnot available
KeywordsRespite careCentralityNursingCaregiver burdenFamily caregiversHealth carePsychologyBusinessMedicineEconomic growthDementia

Abstract

fetched live from OpenAlex

Policy to support informal caregivers is a critical health policy issue in Canada. Lessons may be learned from the perspectives and experience in the child health field with applicability for all cared-for persons and their informal caregivers. Familycentred care addresses the centrality of the family caregiver in the design and delivery of health services. A life course approach focuses on key periods of transition and downstream effects facing caregivers over their lifetime. The medical home model where care delivery is more coordinated offers potential direct cost savings for both family caregivers and the healthcare system. Models of pediatric home care that focus on promoting caregiver capacity and integration of unregulated providers show the promise of being acceptable and sustainable solutions to increasing demands for caregiver respite. Finally, a number of assumptions that are somewhat unique to the pediatric caregiver experience are explored and/or challenged. These lessons and assumptions may provide insight for policymakers in the development of systems and supports for all cared-for persons and their caregivers in Canada.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0120.012
Scholarly communication0.0090.007
Open science0.0040.006
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0040.000

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 head0.232
GPT teacher head0.450
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations10
Published2015
Admission routes3
Has abstractyes

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