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Record W2182226872

Rural surgical services in two Canadian provinces.

2006· article· en· W2182226872 on OpenAlexaboutno aff
Stuart Iglesias, Joshua Tepper, Erik Ellehoj, Brendan Barrett, Peter Hutten‐Czapski, Kir Luong, William G. Pollett

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRural areaRural healthFamily medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: Contrast alternative health delivery systems and the use of differently trained physician providers in the supply of surgical services to rural residents in 2 Canadian provinces. METHODS: Four surgical procedures (carpal tunnel release, inguinal herniorrhaphy, appendectomy and cholecystectomy) provided to rural residents of Alberta and Northern Ontario were identified between 1997/98 and 2001/02. Surgical staff were identified as specialists or non-specialists. Rural populations were mapped into the catchment areas of rural acute care facilities. Rural surgical programs were characterized by the level of surgical service available locally. RESULTS: Alberta and Northern Ontario have a similar number of rural surgical programs staffed by Canadian-certified general surgeons (10 and 12, respectively). However, Alberta has 27 smaller rural surgical programs staffed by non-specialist surgeons and Northern Ontario has only 4. These non-specialist surgeons play a significant role in Alberta, often in collaboration with specialist surgeons. In Northern Ontario the non-specialist surgeons play a minor role. The small rural surgical programs in Northern Ontario that are staffed by specialist surgeons are significantly more successful in retaining the local surgical caseload compared with similar programs in Alberta. CONCLUSIONS: The principal differences between Alberta and Northern Ontario in the delivery of rural surgical services are the greater number of small rural surgical programs in Alberta, and the substantial role of non-specialist surgical staff in these programs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.290
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.356
Teacher spread0.334 · 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.

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

Citations16
Published2006
Admission routes1
Has abstractyes

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