MétaCan
Menu
← Back to cohort
Record W2024128277 · doi:10.1136/bmj.c3438

US medical schools and teaching hospitals are still "struggling" financially

2010· article· en· W2024128277 on OpenAlexaboutno aff
J. H. Tanne

Bibliographic record

VenueBMJ · 2010
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationMathematics educationPsychologyMedicine

Abstract

fetched live from OpenAlex

Most US medical schools and academic medical centres have not made the systemic changes needed to maintain their financial health in the wake of the 2008 recession, says a report from the Association of American Medical Colleges. It says: “The near collapse of the US financial system in 2008 . . . did not spare the US healthcare system, including medical schools and teaching hospitals. Two years later, the media is still filled with reports about schools and hospitals that are struggling to survive.” One health system executive comments in the report: “Taking steps to protect the status quo ignores reality.” The association represents all 133 accredited US and 17 Canadian medical schools, about …

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)0.009
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 score0.183
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0100.005
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0270.005

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 head0.026
GPT teacher head0.429
Teacher spread0.403 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueBMJ→Same topicPrimary Care and Health Outcomes→French-language works237,207→