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Record W1990698384 · doi:10.3928/00220124-20111115-03

Preliminary Exploration of the Use of a Medical Malpractice Self-Study Module

2011· article· en· W1990698384 on OpenAlexaff
Deborah L. Nellis, Lynn George

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2011
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsSt. Clair College
Fundersnot available
KeywordsMalpracticeMedical malpracticePsychologyMedical emergencyMedicineLawPolitical science

Abstract

fetched live from OpenAlex

This study was conducted to determine the effectiveness of an educational module on medical malpractice litigation and the use of evidence-based practice guidelines. Data regarding knowledge acquisition, ease of use, and the perceived value of the educational module were collected. A pretest-posttest design was used. There was a statistically significant difference in the proportion of participants who responded correctly to the posttest items after viewing the educational program (p < .05). Data from this study indicated that this self-study module was a valuable tool for education on the specified content. This study also provides evidence of the effectiveness of integrating theory, clinical inquiry, and evidence-based practice into a self-paced educational program about medical malpractice litigation. Evaluation of a self-paced educational program contributes to the body of knowledge on the use of educational strategies to promote patient safety and reduce liability.

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)0.007
metaresearch head score (Gemma)0.029
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 score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.142
GPT teacher head0.466
Teacher spread0.325 · 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
Published2011
Admission routes1
Has abstractyes

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