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Record W2100445121 · doi:10.2190/hs.39.1.i

Users and Suppliers of Physician Services: A Tale of Two Populations

2009· preprint· en· W2100445121 on OpenAlexaffabout
Frank T. Denton, Amiram Gafni, Byron G. Spencer

Bibliographic record

VenueInternational Journal of Health Services · 2009
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEconomic shortagePopulationPhysician supplyBusinessHealth carePopulation ageingVariable (mathematics)Population growthPopulation sizeDemographic economicsActuarial scienceMedicineEconomicsEconomic growthEnvironmental health

Abstract

fetched live from OpenAlex

Physician shortages and their implications for required increases in the physician population are matters of considerable interest in many health care systems, especially in the light of the widespread phenomenon of population aging. To determine the extent to which shortages exist, one needs to study the population of users of physician services as well as that of the physicians themselves. The authors study both, using the province of Ontario, Canada, as an example. The user population is projected and the implications for requirements calculated, conditional on given utilization rates. On the supplier side, the age and other characteristics of the (active) physician population are examined and patterns of withdrawal investigated. The necessary future growth of supply is calculated, assuming alternative levels of present shortages. The effects of population change on requirements are found to be smaller in the future than in the decade 1981-1991, in the aggregate, not far from the effects in 1991-2001, but highly variable among different categories of physicians.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.005
Scholarly communication0.0050.006
Open science0.0010.005
Research integrity0.0020.002
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 head0.052
GPT teacher head0.350
Teacher spread0.297 · 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

Citations1
Published2009
Admission routes2
Has abstractyes

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