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Record W4396613782 · doi:10.61440/jcsa.2024.v2.07

The Impact of Caregiving on the Health and Quality of Life: A Comparative Population Based Study of Caregivers of Elderly Persons in the USA, UK, Canada and Australia

2024· article· en· W4396613782 on OpenAlexaboutno aff
Ookubo Odion Patrick, Ogbu JC

Bibliographic record

VenueJournal of Clinical Surgery and Anesthesia · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsReactivity (psychology)ChemistryMedicine

Abstract

fetched live from OpenAlex

Caregiving is an important public health issue that affects the health and quality of life of millions of people living in the United States, United Kingdom, Canada, Australia, and the world at large. The caregiver’s responsibilities may increase and change as the recipient’s needs increase, which can cause additional stress on the caregiver. Therefore, this study examines the impact of caregiving on the health and quality of life of caregivers of older adults in the United States, United Kingdom, Australia, and Canada. The caregiving data presented in this study were collected from studies from the United States of America, United Kingdom, Canada, and Australia. Carers in all 4 countries reported high levels of stress, low levels of health, depression, reduced incomes, difficulty making ends meet, social isolation, loneliness, and absenteeism from work. Caregivers sometimes had to leave the workforce altogether and this led to lower government revenue from income taxes. It is an established fact that a reduction in the workforce of a country often leads to a reduction in government revenue from income taxes. Based on the findings, it was recommended among others that there should be an increase in messaging or information that emphasizes both the important role of caregivers and the importance of maintaining their health and wellbeing.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.293
GPT teacher head0.496
Teacher spread0.203 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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