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Alternative Funding for Academic Medicine: Experience at a Canadian Health Sciences Center

2004· article· en· W1973162701 on OpenAlexaffabout
Paul R. Rosenbaum, Sam Shortt, David M. Walker

Bibliographic record

VenueAcademic Medicine · 2004
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsQueen's University
Fundersnot available
KeywordsGovernment (linguistics)Health careAccountabilityHigher educationBusinessSubsidyReimbursementMedical educationPublic relationsMedicinePolitical science

Abstract

fetched live from OpenAlex

In 1994 the School of Medicine of Queen's University in Kingston, Ontario, its clinical teachers, and the three principal teaching hospitals initiated a new approach to funding, the Alternative Funding Plan, a pragmatic response to the inability of fee-for-service billing by clinical faculty to subsidize the academic mission of the health sciences center. The center was funded to provide a package of service and academic deliverables (outputs), rather than on the basis of payment for physician clinical activity (inputs). The new plan required a new governance structure representing stakeholders and raised a number of important issues: how to reconcile the preservation of physician professional autonomy with corporate responsibilities; how to gather requisite information so as to equitably allocate resources; and how to report to the Ontario Ministry of Health and Long-term Care in order to demonstrate accountability. In subsequent iterations of the agreement it was necessary to address issues of flexibility resulting from locked-in funding levels and to devise meaningful performance measures for departments and the center as a whole. The authors conclude that the Alternative Funding Plan represents a successful innovation in funding for an academic health sciences center in that it has created financial stability, as well as modest positive effects for education and research. The Ontario government hopes to replicate the model at the province's other four health sciences centers, and it may have applicability in any jurisdiction in which the costs of medical education outstrip the capacity of faculty clinical earnings.

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)0.012
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.984

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.006
Science and technology studies0.0440.008
Scholarly communication0.0070.002
Open science0.0040.007
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0090.001

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 head0.225
GPT teacher head0.541
Teacher spread0.316 · 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.

Study designObservational
DomainIncentives
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

Citations2
Published2004
Admission routes2
Has abstractyes

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