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Record W6993238723

"A Nurse is not a Nurse is not a Nurse": The Social Construction of Skill Among Internationally Educated Nurses Through the Lens of Feminist Economy and Disablement

2025· other· en· W6993238723 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsSocial constructionismWelfareHealth carePublic policyMental healthPower (physics)Welfare reformEmotional laborAccommodation
DOInot available

Abstract

fetched live from OpenAlex

This dissertation examines policies from 2000 to 2024 regarding the assessment of internationally educated nurses' (IENs) skills, including credentials and work experience needed to enter Canadian nursing. I show that federal and provincial initiatives, like the National Nurses Assessment Service and fair access legislation, have made the process stricter, longer, and more expensive. As a result, many IENs are pushed into lower-tiered healthcare jobs, such as personal support work, characterized by high job insecurity, low wages, and increased risks of disability, including injuries, illnesses, and mental health distress. I argue that IENs' downward occupational mobility extends beyond a policy failure or racial biases; it is intricately connected to the racialized and feminized segmentation of care work, underpinning Canada’s development as a settler-colonial capitalist state. It is a continuation of a gatekeeping mechanism where (a) white nurses enhance their power and privilege—like better pay, benefits, and social status—by upholding masculinist and colonial beliefs about skill, often marginalizing labour associated with poor, non-white women, and (b) settlers access a pool of easily exploitable labour to fulfill the nation’s social reproductive demands. Unlike past racially explicit exclusions, the current policy uses “managed” integration, marked by selectivity (higher scrutiny) and assimilation (limited to those who approximate Canadian nursing standards). This segmentation is masked by race-neutral policies focusing on public safety and upholding international applicants' rights to fairness. Despite the advantages of racial segmentation processes, I illustrate how the downward occupational mobility of IENs adversely affects their health and the welfare of impoverished communities in their home countries, particularly the Philippines, and is closely linked to neoliberal privatization and declining care standards in Canada. By integrating insights from critical policy analysis, feminist political economy, critical race theory, and critical disability studies, I develop a framework to (a) examine the ideological and socioeconomic interests integration policy supports and (b) advocate for a fundamentally different approach to healthcare organization, specifically, one that challenges the hierarchical classification of skills (i.e., distinguishing between high-skilled and low-skilled jobs) as the mechanism that determines workers' unequal access to compensation, benefits, job security, legal protections, and social status.

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)0.006
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.476
Threshold uncertainty score0.946

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0240.073
Scholarly communication0.0100.007
Open science0.0020.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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
Published2025
Admission routes1
Has abstractyes

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