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Record W2218193128 · doi:10.3148/cjdpr-2014-001

Online Education Improves Canadian Dietitians’ Attitudes and Knowledge Regarding Recommending and Ordering Multivitamin/Mineral Supplements

2014· article· en· W2218193128 on OpenAlexaffvenueabout
Liz da Silva, Rebecca Brody, Laura Byham‐Gray, J. Scott Parrott

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsFraser HealthUniversity of British Columbia
Fundersnot available
KeywordsMultivitaminMedicineMedical educationFamily medicineInternal medicineVitamin

Abstract

fetched live from OpenAlex

PURPOSE: To determine the attitudes and knowledge of Fraser Health registered dietitians (RDs) regarding recommending and ordering multivitamin/mineral supplements prior to and following an online education module. METHODS: The educational intervention consisted of narrated slides with electronic resources. After undergoing external review for face and content validity, 6 attitude questions and a 15-item knowledge test were administered pre- and postintervention. The attitude questionnaire utilized a 5-point Likert scale and had a maximum summative score of 30 points. The knowledge test was worth a maximum of 15 points. RESULTS: Of the eligible RDs (n = 123), 57 (46.3%) completed the study and 55 participants were included in the final analyses. Summative attitude scores were higher on the post-intervention questionnaire compared with the preintervention questionnaire (t = 92.5, P < 0.001). The proportion of correctly answered knowledge questions pre- (78.0% ± 10.0%) to postintervention (mean = 87.4% ± 6.0%) increased significantly (t = 7.16, P < 0.001). CONCLUSIONS: Postintervention, RD attitudes and knowledge improved confirming that the education strategy was effective. Future work should focus on optimizing the module and knowledge questions.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.036
GPT teacher head0.376
Teacher spread0.341 · 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 designNot applicable
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

Citations5
Published2014
Admission routes3
Has abstractyes

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