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Record W2086436034 · doi:10.1300/j013v37n03_01

Meeting the Health Care Needs of Female Crack Users: A Canadian Example

2003· article· en· W2086436034 on OpenAlexaffabout
Jennifer E. Butters, Patricia G. Erickson

Bibliographic record

VenueWomen & Health · 2003
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsHealth careWelfareSocial WelfareNursingMedicineGerontologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Canada is an egalitarian society committed to accessible and comprehensive health care. Although there has been a tendency to assume that its various social welfare programs have improved health conditions for lower income citizens, Canada's record in ensuring health equality remains poorer than expected (Humphries and van Doorslaer, 2000; Wasylenki, 2001). The Canadian Health Act stipulates that all residents of Canada are to have access to medically necessary hospital and physician services based on need and not the ability to pay. However, for marginalized groups such as drug users and the homeless, structural barriers to better health remain. This paper examines the health care needs and experiences of 30 women who were heavily involved in the street life of crack and prostitution in Toronto. Through their ready access to local drop-in clinics and nearby hospitals, the women reported generally positive experiences with the health care system. The study concludes that the women experienced many of the health problems that typify homeless, poorly housed and economically marginalized groups. Both positive and negative experiences with the health care system, and structural barriers that hamper its full utilization, are identified.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
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 head0.066
GPT teacher head0.394
Teacher spread0.328 · 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 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

Citations56
Published2003
Admission routes2
Has abstractyes

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