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Record W2335105546 · doi:10.3148/74.4.2013.198

Nutrition and Shiftwork: Evaluation of New Paramedics’ Knowledge and Attitudes

2013· article· en· W2335105546 on OpenAlexaffvenue
Amanda MacDonald, Jan L. Jensen, Melissa D. Rossiter

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2013
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsNova Scotia Health AuthorityDalhousie UniversityMount Saint Vincent University
Fundersnot available
KeywordsMedicineIntervention (counseling)Family medicineNutrition EducationCohortGerontologyNursing

Abstract

fetched live from OpenAlex

PURPOSE: The effect of an oral education intervention on nutrition knowledge was evaluated in new paramedic employees. The evaluation involved measuring knowledge of and attitudes toward nutrition and shiftwork before and after the directed intervention. METHODS: A convenience sample of 30 new paramedic shiftworkers attended a 15-minute education session focused on nutrition management strategies. This matched cohort study included three self-administered surveys. Survey 1 was completed before education, survey 2 immediately after education, and survey 3 after one month of concurrent post-education and employment experience. Knowledge and attitude scores were analyzed for differences between all surveys. RESULTS: Participants were primary care paramedics, 59% of whom were male. They reported that previously they had not received this type of information or had received only a brief lecture. Mean knowledge scores increased significantly from survey 1 to survey 2; knowledge retention was identified in survey 3. A significant difference was found between surveys 2 and 3 for attitudes toward meal timing; no other significant differences were found between attitude response scores. CONCLUSIONS: The education session was successful in improving shiftwork nutrition knowledge among paramedics. Paramedics' attitudes toward proper nutrition practices were positive before the education intervention.

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)0.004
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 integrity0.0000.001
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 head0.185
GPT teacher head0.474
Teacher spread0.289 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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