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Record W2093670040 · doi:10.1177/0002764211407835

Contracting Out Hospital Support Jobs: The Effects of Poverty Wages, Excessive Workload, and Job Insecurity on Work and Family Life

2011· article· en· W2093670040 on OpenAlexafffundabout
Daniyal Zuberi

Bibliographic record

VenueAmerican Behavioral Scientist · 2011
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOutsourcingWorkloadMultinational corporationLabour economicsWork (physics)NegotiationWageBusinessJob insecurityPovertyIncome SupportDemographic economicsHealth careTemporary workEconomicsEconomic growthPolitical scienceFinanceManagementMarketing

Abstract

fetched live from OpenAlex

Based on in-depth interviews with 70 hospital support workers in Vancouver, Canada, this article describes how the contracting out of their jobs to multinational corporations has had deleterious consequences for these workers, their families, and the health care system. Privatization and outsourcing resulted in a steep initial wage reduction for hospital support staff, decreasing by up to 50% from approximately $18 to $20 per hour to between $9 and $12 per hour—with much weaker job benefits. Despite recent wage increases as a result of Hospital Employees’ Union—led contract negotiations, workers still earn lower hourly wages than they did before contracting out and report challenges making ends meet. The concluding discussion presents the implications of these findings for the sociology of work and health and proposes some policy reforms for mitigating the negative consequences of privatization. The article also describes the beginning of a living-wage movement in Vancouver that has emerged in part as a result of this decision to outsource these hospital support jobs.

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)0.003
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.767

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.004
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.365
Teacher spread0.319 · 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

Citations23
Published2011
Admission routes3
Has abstractyes

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