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A Survey of Physician Training Programs in Risk Management and Communication Skills for Malpractice Prevention

2000· article· en· W1975822629 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Law Medicine & Ethics · 2000
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsInstitute of Health Services and Policy Research
FundersDivision of Graduate EducationU.S. Public Health ServiceHealth Resources and Services Administration
KeywordsMalpracticeLawsuitDefensive medicineMedical malpracticeMedicineHealth carePsychologyFamily medicineMedical emergencyNursingLawPolitical science

Abstract

fetched live from OpenAlex

Malpractice lawsuits serve as a great source of pain, consternation and loss for physicians and patients alike, usually leaving all parties involved in the process with a sense of betrayal. A significant number of physicians will be sued at least once in their career, especially if they practice in some of the more vulnerable specialties. In addition, there is some evidence that the threat of malpractice lawsuits changes the practice style of many physicians, leading to the practice of “defensive medicine” and raises the total cost of health care. Clearly, the prevention of medical malpractice is an issue that deserves considerable attention from physicians and from those who train them. Empirical evidence suggests that medical negligence may play a relatively minor role in malpractice lawsuits. As demonstrated by Localio, et al., one in thirty-five cases of negligence or incompetence actually results in a lawsuit.

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.

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)0.037
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0370.005
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 science0.0000.000
Research integrity0.0000.002
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 head0.345
GPT teacher head0.537
Teacher spread0.192 · 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