MétaCan
Menu
Back to cohort
Record W1474161890 · doi:10.3233/jrs-2010-0501

Making medical practice safer: The role of public policy

2010· article· en· W1474161890 on OpenAlexaff
S. E. D. Shortt, Michael F. Green, Stan Corbett, Laure Paquette, Nadia Zurba, Margaret Darling

Bibliographic record

VenueInternational Journal of Risk & Safety in Medicine · 2010
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsLakehead UniversityQueen's University
Fundersnot available
KeywordsSAFERBusinessMedicineComputer scienceComputer security

Abstract

fetched live from OpenAlex

Health care quality and safety is a public policy issue. In the past, issues of quality of care have been largely delegated to the medical profession. Now, however, governments may wish to assume a more active role and will need to know what governance tools are likely to be effective. This paper reviews the evidence on effectiveness for seven processes for governing the physician sector: informing; guiding; educating; reporting; incentivizing; re-licensing; and punishing. There is good evidence for modest to moderate effectiveness of the first three approaches, scant evidence for the next two, and no empirical data on the final two.

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)0.018
metaresearch head score (Gemma)0.203
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.203
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.053
GPT teacher head0.503
Teacher spread0.450 · 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.

Study designNot applicable
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 venueInternational Journal of Risk & Safety in MedicineSame topicMedical Malpractice and Liability IssuesFrench-language works237,207