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Record W2398458591

Consultations with doctors and nurses.

2005· article· en· W2398458591 on OpenAlexaff
Gisèle Carrière

Bibliographic record

VenuePubMed · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsFamily medicineMedicineNurse practitionersHouse callNursingHealth care
DOInot available

Abstract

fetched live from OpenAlex

Most have family doctor or GP In 2003, an estimated 86% of Canadians aged 12 and older—about 23 million people—reported that they had a “regular” medical doctor. Even if they did not, just over threequarters of people these ages (77%) said they had consulted a family physician or a general practitioner (GP) at least once in the past year. As well, 11% reported having consulted a nurse. Predictably, older Canadians were more likely than younger people to have seen or talked to a family physician or GP. By contrast, having had at least one consultation with a nurse was more likely in early adulthood. At nearly all ages, the proportion of females who consulted family physicians/GPs or nurses at least once was greater than that for males. Nurse visits among seniors were the exception. While women aged 65 or older CONSULTATIONS WITH DOCTORS AND NURSES by Gisele Carriere

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)0.001
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.130
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.1300.024

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 head0.016
GPT teacher head0.213
Teacher spread0.197 · 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

Citations14
Published2005
Admission routes1
Has abstractyes

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