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Record W4283732034 · doi:10.1177/08404704221104435

Looking beyond parity: Gender wage gaps and the leadership labyrinth in the Canadian healthcare management workforce

2022· article· en· W4283732034 on OpenAlexaffabout
Neeru Gupta, Sarah Balcom, Paramdeep Singh

Bibliographic record

VenueHealthcare Management Forum · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsWorkforceHealth careWageLiberian dollarDemographic economicsEarningsGender pay gapBusinessEquity (law)CensusPolitical scienceLabour economicsEconomicsEconomic growthSociologyDemographyAccountingFinancePopulation

Abstract

fetched live from OpenAlex

It is important for health organizations to monitor progress toward gender equity and inclusion goals among health human resources. Within the Canadian healthcare management workforce, however, recent investigations are lacking. This study examines gender differences in composition and compensation among health leadership in Canada using national census data. Findings show that although women represent over half (57%) of senior managers in health and social services, the pipeline from middle management (72%) suggests persistent career barriers disproportionately affect women. Women health and social care managers' earnings averaged $0.83-.89 for every dollar that a man earned. The gender wage gap remained statistically significant, with women health managers earning 12-20% less than men, after adjusting for age, education and other characteristics. Dynamic decomposition analyses highlighted that most of the gender wage gap could not be explained within the available data-a finding attributable, at least in part, to (unmeasured and unmeasurable) gender discrimination.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.080
GPT teacher head0.306
Teacher spread0.226 · 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 designTheoretical or conceptual
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

Citations8
Published2022
Admission routes2
Has abstractyes

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