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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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.149
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.011
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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.

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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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