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Record W2070916974 · doi:10.1353/cja.2005.0058

Growing Old Together: The Influence of Population and Workforce Aging on Supply and Use of Family Physicians

2005· article· en· W2070916974 on OpenAlexaff
Diane Watson, Robert J. Reid, Noralou P. Roos, Petra Heppner

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2005
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of ManitobaVancouver Hospital and Health Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsWorkforcePopulation ageingBusinessAging in the American workforcePopulationGerontologyEconomic growthMedicineEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

Canadians have expressed concern that access to a family physician (FP) has declined precipitously. Yet FP-topopulation ratios remained relatively stable over the last decade, and there were perceptions of physician surpluses, at least in urban centres, 10 years ago. We evaluated whether demographic changes among patients and FPs, and in the volume of care received and provided over the period, contribute to this paradox. Given the relationship between age and FP use in fiscal year 1991/1992, an aging population should have been associated with a 2 per cent increase in visits by 2000/2001. Likewise, given the relationship between FP age and workloads in 1991/1992, an aging workforce should have been associated with a 12 per cent increase in service provision a decade later. Yet visit rates and average FP workloads remained unchanged. There was an increase in age-specific rates of FP use among older adults and a decline in rates among the young, and an increase in age-specific workloads such that older FPs provided many more services than their predecessors (30%) and younger FPs provided many fewer (20%). In terms of impact on future requirements for FPs, both changes in age-specific rates of use, and changes in age-specific patterns of FP productivity, trump population aging as key drivers.

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 score0.267
Threshold uncertainty score0.859

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.310
Teacher spread0.283 · 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

Citations14
Published2005
Admission routes1
Has abstractyes

Explore more

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