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Unpaid informal carers: The ‘shadow’ workforce in health care

2020· book-chapter· en· W4231859020 on OpenAlexaboutno aff
A. Paul Williams, Janet Lum

Bibliographic record

VenuePolicy Press eBooks · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceShadow (psychology)Closure (psychology)Perspective (graphical)Public relationsIndependence (probability theory)Health careNursingPolitical scienceBusinessEconomic growthPsychologyMedicineEconomics

Abstract

fetched live from OpenAlex

Much of the international literature on health human resources focuses on highly trained, regulated and visible professionals with exclusionary social closure in neo-Weberian terms, such as doctors and nurses. However, researchers and policy makers are now paying more attention to the increasingly important role played by less well-trained, often unregulated, and less visible occupations such as personal support workers. Beyond these categories of paid workers exists another mostly uncharted health human resource: unpaid, little trained, largely unregulated and invisible informal carers. They include the family, friends and neighbours who provide the bulk of everyday care required to support the well being and independence of growing numbers of people facing multiple chronic health and social needs in community settings. Focusing on Canada, this chapter documents the characteristics and contributions of informal carers, and highlights the challenging realities of informal caregiving – both from the perspective of carers and policy makers considering how best to support and encourage unpaid, informal carers without driving up formal health system costs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.142
GPT teacher head0.396
Teacher spread0.253 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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