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Record W2007198729 · doi:10.1186/1472-6874-4-s1-s33

Health Care Utilization by Canadian Women

2004· article· en· W2007198729 on OpenAlexaffabout
Arminée Kazanjian, Denise Morettin, Robert Cho

Bibliographic record

VenueBMC Women s Health · 2004
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsHealth CanadaInstitute of Health Services and Policy ResearchUniversity of British Columbia
Fundersnot available
KeywordsHealth careNursingBusinessPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

HEALTH ISSUES: While women are reported to be more frequent users of health services in Canada, differences in women's and men's health care utilization have not been fully explored. To provide an overview on women's healthcare utilization, we selected two key issues that are important for public policy purposes: access to care and patterns of utilization. These issues are examined using primarily data from the 1998/99 National Population Health Survey, complemented by the 2000 Canadian Community Health Survey and the 2001 Health Service Access Survey. KEY FINDINGS: * Women are twice as likely as men to report a regular family physician, but that proportion is very low (15.8%).* Women report significantly shorter specialist wait times (20.9 days) than men (55.4 days) for mental health, while the reverse is true for asthma and other breathing conditions (10.8 for men, 78.8 for women).* Reported mean wait times are significantly lower for men than for women pertaining to overall diagnostic tests: for MRI, 70.3 days for women compared to 29.1 days for men. DATA GAPS AND RECOMMENDATIONS: * Measurement of possible system bias and its implication for equitable and quality healthcare for women requires larger provincial samples of the national surveys, along with a longitudinal design.* Either a national database on preventive services, or better alignment of provincial databases pertaining to health promotion and preventive services, is needed to facilitate data linkage with national surveys to undertake longitudinal studies that support gender based analyses.en are reported to be more frequent users of health services in Canada, differences in women's and men's health care utilization have not been fully explored. To provide an overview on women's healthcare utilization, we selected two key issues that are important for public policy purposes: access to care and patterns of utilization. These issues are examined using primarily data from the 1998/99 National Population Health Survey, complemented by the 2000 Canadian Community Health Survey and the 2001 Health Service Access Survey. KEY FINDINGS: * Women are twice as likely as men to report a regular family physician, but that proportion is very low (15.8%).* Women report significantly shorter specialist wait times (20.9 days) than men (55.4 days) for mental health, while the reverse is true for asthma and other breathing conditions (10.8 for men, 78.8 for women).* Reported mean wait times are significantly lower for men than for women pertaining to overall diagnostic tests: for MRI, 70.3 days for women compared to 29.1 days for men. DATA GAPS AND RECOMMENDATIONS: * Measurement of possible system bias and its implication for equitable and quality healthcare for women requires larger provincial samples of the national surveys, along with a longitudinal design.* Either a national database on preventive services, or better alignment of provincial databases pertaining to health promotion and preventive services, is needed to facilitate data linkage with national surveys to undertake longitudinal studies that support gender based analyses.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.001

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 head0.062
GPT teacher head0.422
Teacher spread0.360 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations38
Published2004
Admission routes2
Has abstractyes

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