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Quality Assurance and Risk Management: A Survey of Dental Schools and Recommendations for Integrated Program Management

2002· article· en· W1945471947 on OpenAlexaboutno aff
Richard Fredekind, Eve Cuny, Nader Nadershahi

Bibliographic record

VenueJournal of Dental Education · 2002
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsQuality assuranceQuality managementRisk managementQuality (philosophy)Medical educationBusinessProcess managementMedicineOperations managementManagement systemEngineeringMarketing

Abstract

fetched live from OpenAlex

Quality assurance (QA) and risk management (RM) programs are intended to improve patient care, meet accreditation standards, and ensure compliance with liability insurance policies. The purpose of this project was to obtain and disseminate information on whether dental schools integrate QA and RM and what mechanisms have been most effective in measuring accomplishments in these programs. All sixty-five U.S. and Canadian dental schools were sent a twenty-nine-item survey, and forty-six (71 percent) schools responded. The main findings are as follows: 66 percent had a written QA program combined with a QA committee; 95 percent received administrative support; there was wide variation in the makeup of the QA committee; many institutions reported significant changes resulting from the QA program; and over half of the respondents merged QA and RM in some fashion. To develop or maintain an effective QA/RM program, the authors propose the following: obtain active support from the dean; develop goals and mission/vision statements; include trained personnel on the committee; establish wide levels of involvement in the QA program; develop QA measurements to ensure compliance with institutionally developed standards of patient care; and establish continuous cycles of improvement.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.261
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.000
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 head0.097
GPT teacher head0.496
Teacher spread0.399 · 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.

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

Citations10
Published2002
Admission routes1
Has abstractyes

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