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Record W2137997710 · doi:10.12927/hcpol.2006.18340

Health Status and Healthcare Use Patterns of Rural, Northern and Urban Manitobans: Is Romanow Right?

2006· article· en· W2137997710 on OpenAlexaffvenueabout
Patricia J. Martens, Randy Fransoo, Charles Burchill, Elaine Burland

Bibliographic record

VenueHealthcare policy · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBiostatisticsHealth careSociologyEpidemiologyPolitical scienceSocial scienceMedicinePublic healthNursingLaw

Abstract

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Objective: To compare health status and healthcare services use of rural, northern and urban Manitobans.Method: Using anonymized administrative claims data derived from the Population Health Research Data Repository housed at the Manitoba Centre for Health Policy, four Manitoba regions were compared -Winnipeg, Brandon, Rural South and North -for 1996/97-2000/01.Indicators include mortality and morbidity, ambulatory physician visit and specialist consultation rates, prevention/screening rates, selected surgery rates (cardiac catheterization, coronary artery bypass graft surgery, hip replacement) and "discretionary" surgery rates (tonsillectomy/adenoidectomy, Caesarean section, hysterectomy).Rates were annualized, directly standardized to the 1996 provincial population, and statistically tested for differences among the regions using Hotelling' s T 2 statistic.Results: Mortality and morbidity are high in the North, but the Rural South is average (except for high rates of injury mortality and stroke).Rural South and North have low ambulatory physician visits and specialist consultation rates, but high hospitalization rates compared to Brandon and Winnipeg.In prevention/screening rates, Rural South is variable and the North is low.For surgery rates, Rural South is variable, North is average, Brandon has below-average surgical rates but high rates of discretionary procedures, and Winnipeg has high surgical rates but low discretionary procedures.Thus, "urban" is not necessarily synonymous with good health and better access to services, nor is "rural" or "remote" synonymous with poor health and inadequate healthcare. RésuméObjectif : Comparer l' état de santé et le recours aux services de soins de santé des Manitobains des zones rurales, nordiques et urbaines.Méthode : À l' aide de données administratives anonymisées tirées du Population Health Research Data Repository du Manitoba Centre for Health Policy, on a comparé quatre régions du Manitoba, soit celles de Winnipeg, de Brandon, du Sud rural et du Nord, pour la période de 1996-1997 à 2000-2001.Les indicateurs comprennent la mortalité et la morbidité, les taux de visites ambulatoires de médecins et de consulta-Health Status and Healthcare Use Patterns of Rural, Northern and Urban Manitobans Sources of data for the research Anonymized individual-level records of all residents of Manitoba (including First Nations peoples) were used for this study and were obtained from the Population Health Research Data Repository housed at MCHP.This Repository contains such databases as medical billing claims, hospital discharge abstracts and vital statistics (mortality) linkable at the individual level.All files are de-identified, with names and addresses removed prior to use by MCHP, but the geographical location and demographic information such as age and sex are available.Ethical approval for the study was obtained from the Health Research Ethics Board of the Faculty of Medicine, and was reviewed by the Health Information Privacy Committee of Manitoba. Indicators and statistical tests for comparisonVarious indicators were selected to represent regional mortality, morbidity and healthcare use (prevention/screening, physician and hospital use, surgical procedure rates).

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)0.000
metaresearch head score (Gemma)0.001
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 score0.998
Threshold uncertainty score1.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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

Citations13
Published2006
Admission routes3
Has abstractyes

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