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Record W139859639

Intersectionalities of influence: researching the health of immigrant and refugee women.

2004· article· en· W139859639 on OpenAlexaff
Sepali Guruge, Nazilla Khanlou

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOperationalizationRefugeeParticipatory action researchScholarshipImmigrationSociologyDiversity (politics)Mental healthGender studiesAction (physics)Identity (music)PsychologyPolitical scienceEpistemologyPsychotherapist
DOInot available

Abstract

fetched live from OpenAlex

There is a growing recognition of the complexity surrounding multiple axes or dimensions of social identity and how they intersect to influence the health of immigrant and refugee women. The concept of intersectionalities of influence is particularly relevant in addressing diversity in nursing research. The purpose of this paper is to theorize and operationalize the concept in mental health promotion research with immigrant and refugee women. At the conceptual level, the authors propose an approach to inquiry that is informed by critical scholarship and draws from postcolonial and feminist perspectives. At the operational level, they apply an ecosystemic framework to help locate individual health issues within the familial, community, and social realms. They introduce Participatory Action Research as a way of putting these concepts into action within the research process. Their aim is to introduce a new way of inquiry that can benefit immigrant and refugee women while furthering the nursing agenda for community-based research.

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)0.011
metaresearch head score (Gemma)0.013
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: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0080.013
Scholarly communication0.0070.005
Open science0.0010.013
Research integrity0.0020.002
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 head0.392
GPT teacher head0.570
Teacher spread0.178 · 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

Citations119
Published2004
Admission routes1
Has abstractyes

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