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Record W4296645304 · doi:10.36367/ntqr.11.2022.e554

Nobody listens, nobody wants to hear you: Access to healthcare/social services for women in Canada

2022· article· en· W4296645304 on OpenAlexafffundabout
Pilar Camargo‐Plazas, Jennifer Waite, Michaela Sparringa, Martha M. Whitfield, Lenora Duhn

Bibliographic record

VenueNew Trends in Qualitative Research · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHealth carePhotovoicePovertynobodyPsychologySociologyPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

In Canada, an unacceptable number of women live below the poverty threshold. Some subgroups of women, such as Indigenous, visible minorities, immigrants and refugees, older adults, and single mothers are more likely to live in poverty, as they face multiple systemic barriers preventing their financial stability. Further, socioeconomic status, employment, gender, and access to healthcare and social services negatively impact women’s well-being and health. Yet little is known about how these factors affect healthcare behaviours and experiences for women living on a low income. Our goal is to describe and understand how gender and income influence access to healthcare and social services for women living on a low income. Methods: Partnered with a not-for-profit organization, we explored the experiences of women living on a low income in Kingston, Canada. Using participatory, art-based research and hermeneutic phenomenological approaches, our data collection methods included photovoice, semi-structured interviews and culture circles. A purposive sample was recruited. Analysis was conducted following the social determinants of health framework by Loppie-Reading and Wien. Results: Participants perceived the healthcare and social services systems as unnecessarily complex, disrespectful, and dismissive–one where they are mere spectators without voice. They do not feel heard. They also identified problematic issues regarding living conditions, housing, and fresh food. Despite these experiences, participants are resilient and optimistic. Implications: Learning from participants has indicated priority issues and potential, pragmatic solutions to begin incremental improvements. Changing system design to enable self-selection of food items is one example. Conclusion: For an individual to feel others view them as unworthy of care, especially if those ‘others’ are the care providers, is ethically and morally distressing–and it certainly does not invite system-use. While our early findings reveal considerable system improvements are required, we are inspired by and can learn from the strength of the participants.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.071
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0360.008
Scholarly communication0.0070.002
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.336
GPT teacher head0.565
Teacher spread0.229 · 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

Citations3
Published2022
Admission routes3
Has abstractyes

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