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Quality assurance and dental hygiene

2003· article· en· W2125933900 on OpenAlexaffabout
Ben iacute tez Sonia E.

Bibliographic record

VenueInternational Journal of Dental Hygiene · 2003
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQuality assuranceDental hygieneMedicineQuality (philosophy)HygienePublic healthHealth careMedical educationEnvironmental healthNursingPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Dental hygiene in Canada has experienced significant growth. It has shifted from an emerging occupation to a regulated health profession in several jurisdictions. Many achievements may be attributed to this growth, including self-regulation and a national code of ethics. However, the majority of Canadian dental hygienists are relying on traditional, outdated and ineffective quality assurance mechanisms, such as mandatory continuing education requirements. In the interests of public protection, dental hygiene needs to ensure that the quality assurance activities required from its members are effective, valid and reliable. Quality assurance in health care continues to undergo modifications that better reflect the public's need for competent, ethical, safe and appropriate health care. Dental hygienists and dental hygiene regulatory bodies are challenged to find valid, reliable and effective methods of quality assurance. This paper discusses some of the developments in quality assurance in health care as well as some of the key and significant achievements of dental hygiene in Canada. The use of quality assurance mechanisms currently used in dental hygiene in Canada is also discussed. The paper concludes with a discussion on the potential barriers and issues that the profession may face when attempting to incorporate suitable quality assurance activities into daily dental hygiene practice.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.452
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.

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

Citations7
Published2003
Admission routes2
Has abstractyes

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