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Record W2956910214 · doi:10.1177/0844562119859138

Spaces of Exclusion: Safety, Stigma, and Surveillance of Mothers Experiencing Homelessness

2019· article· en· W2956910214 on OpenAlexaffvenueabout
Sarah Benbow, Cheryl Forchuk, Hélène Berman, Carolyne Gorlick, Catherine Ward‐Griffin

Bibliographic record

VenueCanadian Journal of Nursing Research · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsThe King's UniversityWestern UniversityUniversity of New BrunswickFanshawe College
Fundersnot available
KeywordsSocial exclusionStigma (botany)NarrativePovertyCriminologySocial isolationPsychologySocial psychologySociologyGender studiesPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Lack of affordable housing, poverty, and intimate partner violence are among the most common reasons for homelessness among mothers and their children in Canada. Mothers experience social exclusion in compounding and debilitating ways. In the literature on social exclusion and health, rarely is safety recognized as a prominent component of social exclusion. The purpose of this critical narrative study was to better understand the unique narratives of social exclusion for mothers experiencing homelessness in Ontario. A critical narrative methodology with an intersectional lens was used. Twenty-six ( N = 26) mothers participated in the study. The overarching finding of unsafe spaces represents the unique forms of exclusion from safety participants experienced in public and private spaces. Emerging out of this overarching category are two intertwined subcategories of (a) exclusion from safety and (b) stigma: public surveillance and discrimination. Participants’ narratives of exclusion from safety signify an ecosystem of unsafe spaces. The findings illuminate and reiterate the imperative for nurses to recognize that safety is a human right and is foundational for health. Nurses can use critical self-reflection and challenge the inherent “nursing gaze” to promote spaces of support rather than surveillance and engage in political advocacy to address structural inequalities, such as gender-based violence.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.095
GPT teacher head0.460
Teacher spread0.365 · 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.

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

Citations21
Published2019
Admission routes3
Has abstractyes

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