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Record W2217206591

Papanicolaou Tests in Canada and the US - Does Type of Insurance Matter?

2007· article· en· W2217206591 on OpenAlexaboutno aff
Stephan F. Gohmann

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyProbit modelProbitPopulationSample (material)Pap testPapanicolaou stainHealth careEstimationGeographyStatisticsDemographic economicsMedicineEconometricsEconomicsSociologyEconomic growthMathematicsCervical cancer
DOInot available

Abstract

fetched live from OpenAlex

Objectives: The health care systems in the United States and Canada differ quite dramatically. Canadians are covered by a universal health care system while residents of the United States, if they are insured, obtain their insurance from various private or public sources. This study examines how the use of Papanicolaou test by women differs in the United States and Canada using data from Joint Canada/United States Survey of Health (JCUSH). Methodology: The probability of having the Pap smear is estimated separately for Canadian and American women using probit regressions. A question arises as to whether differences in the probability of the pap test are a result of (1) differences in the demographics between Canadian and American women or (2) differences in the estimated coefficients. A Blinder/Oaxaca type decomposition is used to determine the influence of each. Gomulka and Stern expanded this methodology for a dichotomous dependent variable using probit estimation. In their analysis, the probit estimates are transformed into changes arising from a change in the coefficients and changes arising from a change in the population. The Gomulka and Stern method is applied to this data. The analysis has two parts. The first part examines the full sample of American and Canadian women between the ages of 18 to 65 and a subsample of those under age 30. In the full sample, 87% of the Canadians have had a pap smear in the past 3 years (60% in the past year) compared to 93% of the American women (71% within the past year). However, for women under age 30, where the recommendation is for a pap smear every year, the differences are reversed with 79% of Canadians and 76% of Americans receiving the test. In the second analysis, privately insured Americans are compared to Canadians. This allows us to compare how the Canadian national health insurance and US private insurance differ in their influence the use of pap smears. Results: For the full sample, the decomposition shows that the differences between the Canadian and US women mostly occur because of differences in coefficients and not the distributions. For women less than 30 years old, the differences are due to both differences in distributions and coefficients. For insured US women the coefficient effect was even stronger. The only significant difference between US and Canadian women who did not have the test was that 6% of the Canadian women reported that they did not know where to go for the test (vs. 1% of the US). Also 91% of the US women compared to 85% of the Canadian women reported that their doctor recommended the test. Conclusions: Overall, women in the US health care system are more likely to have pap smears and the main driver is differences in the coefficients. Given that fewer Canadian physicians recommend the test, the differences can be attributed to differences in how the systems value the test.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.011
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.035
GPT teacher head0.373
Teacher spread0.338 · 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 designObservational
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
Published2007
Admission routes1
Has abstractyes

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