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Record W1983726022 · doi:10.1136/ebn.8.4.128

“Multiple margins” (being older, a woman, or a visible minority) constrained older women’s access to Canadian health care

2005· letter· en· W1983726022 on OpenAlexaffabout
Diane Pirner

Bibliographic record

VenueEvidence-Based Nursing · 2005
Typeletter
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLonelinessMedicineFemininityOlder peopleHealth carePsychologyDemographyGerontologyPsychiatryPolitical scienceSociologyPsychoanalysis

Abstract

fetched live from OpenAlex

Kinch JL, Jakubec S. Out of the multiple margins: older women managing their health care. Can J Nurs Res 2004;36:90–108.[OpenUrl][1][PubMed][2] Q What are older women’s experiences with the Canadian healthcare system? How do they manage their health and access health care? A feminist phenomenological study. Calgary, Alberta, Canada. 32 Canadian women who were 65–83 years of age. 14 were Caucasian, 11 Ismaili, 3 Aboriginal, and 4 Japanese. Participants were interviewed (for 1.5 to 2.5 h) in 5 small groups of 3–11 women. A follow up interview was held with 5 members of 1 group. Interviews were audiotaped, transcribed, and analysed for emergent themes. 4 themes were identified within and across the interviews. Femininity, relationships, and means of support. The women were intrigued by the concept of sharing their stories in an organised discussion (eg, “We always get together and we sit around and talk, but never discussing the …the personal things”). Examples of “personal” matters included the loneliness they had endured after spending years in various relationships in their working and personal lives. They made a connection between extreme loneliness and … [1]: {openurl}?query=rft.jtitle%253DCanadian%2BJournal%2Bof%2BNursing%2BResearch%26rft.stitle%253DCan%2BJ%2BNurs%2BRes%26rft.aulast%253DKinch%26rft.auinit1%253DJ.%2BL.%26rft.volume%253D36%26rft.issue%253D4%26rft.spage%253D90%26rft.epage%253D108%26rft.atitle%253DOut%2Bof%2Bthe%2Bmultiple%2Bmargins%253A%2Bolder%2Bwomen%2Bmanaging%2Btheir%2Bhealth%2Bcare.%26rft_id%253Dinfo%253Apmid%252F15739939%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=15739939&link_type=MED&atom=%2Febnurs%2F8%2F4%2F128.atom

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.478
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.097
GPT teacher head0.399
Teacher spread0.302 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2005
Admission routes2
Has abstractyes

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