MétaCan
Menu
Back to cohort

Pay equity and nursing in Ontario: ten years later

2000· article· en· W2016282264 on OpenAlexaffabout
Rita Schreiber, Elisheva Tamar Anne Nemetz

Bibliographic record

VenueInternational Nursing Review · 2000
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLegislationPay EquityEquity (law)PoliticsPaceSocial equalityPolitical scienceHealth careIntergenerational equityCollective bargainingNursingPublic administrationEconomic growthEconomicsMedicineLabour economicsLawGeographySustainability

Abstract

fetched live from OpenAlex

'Nurses have never been compensated in accordance with their central role in health care' (Schreiber 1994), reflecting the generalized and well-documented under-valuing of the work of women (Schreiber 1993). Pay equity legislation, passed in Ontario, Canada in 1987, designed to correct gender wage discrimination, created great optimism that the under-valuing of nurses' work might be ending. Nonetheless, this has not been the case, as the social, political, and economic climate has not kept pace with the speed and enormity of social change necessary to enact the intent of the legislation. Indeed, gains in nurses' wages have been directly offset by significant lay-offs. In this paper, we examine the issues surrounding the implementation of pay equity legislation in Ontario, Canada, along with analysis and implications of these issues, drawn from 10 years of experience. In addition, we highlight lessons that can be learned from the Ontario experience.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.170
Threshold uncertainty score0.962

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0060.003
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.074
GPT teacher head0.351
Teacher spread0.277 · 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

Citations3
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueInternational Nursing ReviewSame topicHealthcare Policy and ManagementFrench-language works237,207