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Record W1551388594

Tackling the issue of access: Situating place within immigrant women's experiences of health and health care (Ontario)

2004· article· en· W1551388594 on OpenAlexaboutno aff
Jillian C. Paul

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2004
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationHealth careSociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Access to health care services is an essential element to immigrant women receiving the care that they need. However, there are barriers that women face as immigrants in a new community. Language, cultural awareness and household responsibilities are issues that a significant number of immigrant women encounter when accessing health care services. There are significant gaps within geographic literature pertaining specifically to marginalized populations and health care experiences. More recent work is beginning to emerge that examines the social and behavioural aspects of health and health care. This study intends to contribute to this growing body of literature aimed at understanding the experiences of immigrant women by examining the role of place within the construction of health care services. Using this concept of place, a theoretical framework was established to answer these questions: How do immigrant women experience the health care system and the services that it offers? How do immigrant women interact with the geographies of health? And finally, what are the service providers doing to alleviate potential barriers to health care for immigrant women? A qualitative methodology was pursued and a community collaborative approach was taken. Using open-ended questions, I interviewed ten health care professionals and conducted two focus groups with Vietnamese and Tamil speaking women. Participant observation was also carried out during this research process. These methods were proven to be useful in attaining in-depth, personal experiences. All of the research was carried out in collaboration with the Immigrant Women’s Centre in downtown Toronto, Ontario. This Centre provides health care to the growing population of immigrant women in the Toronto area and staffs counselors with various cultural backgrounds and many different languages. The Centre services these communities by giving them access to a Winnebago that travels to various locations within the Toronto area, called the Mobile Health Unit (MHU). Findings show that immigrant women’s negative experiences towards health care places arise mainly because the system is structured to exclude those who do not follow institutional norms related to health care practices. The MHU is a service that crosses the barriers that many immigrant women face in accessing appropriate health care. It provides a culturally safe environment where language factors are ameliorated and where women can get the health care that they need. Moreover, it is a place that has a foundation based on the values and beliefs of immigrant women, which is shown to be essential in the delivery process. Communication is integral to these assumptions. Understanding the interrelated diversities of immigrant women’s experiences with health care is vital to creating policy and practice that is inclusive and responsive. Policy makers, however, have tended to exclude the understandings of marginalized populations. Only by incorporating the voices of immigrant women will the Canadian health care system truly move towards providing quality care to all members of its diverse population. Policy makers must look to successes of grassroots initiatives like the Immigrant Women’s Health Centre and the MHU in order to create more accessible and inclusive health care models.

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)0.003
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 score0.639
Threshold uncertainty score0.717

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.011
Scholarly communication0.0050.002
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.347
Teacher spread0.310 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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