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Record W4285664160 · doi:10.51952/9781447352112.ch002

Health professionals, support workers and the precariat

2020· book-chapter· en· W4285664160 on OpenAlexaboutno aff
Mike Saks, Katherine Zagrodney

Bibliographic record

VenuePolicy Press eBooks · 2020
Typebook-chapter
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHealth professionalsBusinessPolitical scienceEconomic growthHealth careEconomics

Abstract

fetched live from OpenAlex

Following a neo-Weberian theoretical perspective, with reference to neo-Marxist analyses, this chapter considers the position of health support workers in the market in neo-liberal societies – with a particular focus empirically on a cross-country comparison between the United Kingdom and Canada. It discusses the role of health support workers holistically in the context of the wider range of health professionals with whom they work. Health professions themselves have been claimed in recent years to have been deprofessionalised or proletarianised. However, it is argued here that such trends are overstated and there is still typically a large gulf between the working conditions of this group of health professional occupations and those of health support workers. The latter are critically considered in terms of the recent interest in depicting such groups as the new precariat. It is argued that there is little doubt that in the United Kingdom and Canada most health support workers can be described as operating in precarious conditions. Nonetheless, doubts are raised as to whether this group will become the self-conscious and cohesive class as envisaged in neo-Marxist theory. The conclusion to the chapter highlights the policy implications of the analysis in light of current debates.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.042

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 studies0.0040.014
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.115
GPT teacher head0.426
Teacher spread0.311 · 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 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
Published2020
Admission routes1
Has abstractyes

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