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The Impact of Quality Assurance Programming: A Comparison of Two Canadian Dental Hygienist Programs

2006· article· en· W2187332294 on OpenAlexaffabout
Joanna Asadoorian, David Locker

Bibliographic record

VenueJournal of Dental Education · 2006
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsQuality assuranceCompetence (human resources)Proxy (statistics)Medical educationContinuing educationPsychologyDental careNursingMedicineFamily medicineComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Quality assurance (QA) and continuing competence (CC) programs aim to ensure acceptable levels of health care provider competence, but it is unknown which program methods most successfully achieve this goal. The objectives of the study reported in this article were to compare two distinct QA/CC programs of Canadian dental hygienists and assess the impact of these two programs on practice behavior change, a proxy measure for quality. British Columbia (BC) and Ontario (ON) were compared because the former mandates continuing education (CE) time requirements. A two-group comparison survey design using a self-administered questionnaire was implemented in randomly selected samples from two jurisdictions. No statistical differences were found in total activity, change opportunities, or change implementation, but ON study subjects participated in significantly more activities that yielded change opportunities and more activities that generated appropriate change implementation, meaning positive and correct approaches to providing care, than BC dental hygienists. Both groups reported implementing change to a similarly high degree. The findings suggest that ON dental hygienists participated in more learning activities that had relevancy to their practice and learning needs than did BC subjects. The findings indicate that the QA program in ON may allow for greater efficiency in professional learning.

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)0.001
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 score0.130
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.024
GPT teacher head0.428
Teacher spread0.404 · 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

Citations39
Published2006
Admission routes2
Has abstractyes

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