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Record W2109471343 · doi:10.3148/75.2.2014.101

Enhanced Dietetics Education Through Collaboration: A Study to Identify Opportunities

2014· article· en· W2109471343 on OpenAlexaffvenueabout
Allison Proudfoot, Daphne Lordly, Barb Anderson, Doris E. Gillis

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2014
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsSt. Francis Xavier UniversityAcadia UniversityMount Saint Vincent University
Fundersnot available
KeywordsInternshipPreceptorMedical educationNova scotiaPsychologyMedicineNursingSociology

Abstract

fetched live from OpenAlex

With the aim of enhancing dietetics education in Nova Scotia, key stakeholders were engaged in identifying current practice issues along with opportunities for collaboration to address them. A survey containing five open-ended questions was distributed by email to a purposive sample of 24 participants affiliated with three universities with dietetics programs. Participants fell into five categories: internship coordinators, dietetics educators, recent internship graduates, current interns, and prospective interns. The response rate was 58%. Data were thematically analyzed through a process of constant comparison. Primary themes emerged, which reflected survey participants' concerns about three current practice issues: province-wide standards, internship placement availability, and the overall educational experience. Additional comments suggested that overall dietetic educational experiences could be improved if relevant clinical experiences were offered and preceptor workloads were accommodated. The creation of province-wide standards for assessing interns' level of competency was perceived to offer multiple benefits, including decreased preceptor workloads. Participants believed that collaborative actions might increase internship placements and improve the overall dietetic internship experience for interns and preceptors.

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)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.273
GPT teacher head0.564
Teacher spread0.291 · 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 designQualitative
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

Citations4
Published2014
Admission routes3
Has abstractyes

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