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Record W2891500572 · doi:10.3148/cjdpr-2018-014

Opportunities and Challenges for Practical Training in Public Health: Insights from Practicum Coordinators in Ontario

2018· article· en· W2891500572 on OpenAlexaffvenueabout
Jessica Wegener, M. PETITCLERC

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPracticumPublic healthCurriculumMedical educationTraining (meteorology)MedicineNursingPsychologyPedagogy

Abstract

fetched live from OpenAlex

Dietetic educators and practicum coordinators (PC) play critical roles in preparing students for practice. Dietitians have made significant progress in the development of educational curricula, competencies, and other resources to support knowledge and skill attainment in public health. There are identified gaps in the literature concerning practical training in sustainable food systems and public health, creating barriers in knowledge exchange and improvements in practicum programs in Canada. This paper discusses the potential opportunities and challenges associated with the number of placements for practical training in public health based on interviews with PCs in Ontario. The findings are limited to the perspectives of 7 PCs with experience in practical training and are a starting point for ongoing evaluation. Identified opportunities within traditional and "emerging settings" for practical training in public health included: the uniqueness of the experience, the potential for students to learn outside their comfort zones, and greater possibilities for dietitians in new roles and settings. Challenges included the need for significant PC engagement with nondietetic preceptors and a narrow view of dietetic practice among some dietitians. Interprofessional teams, emerging settings, and flexible learning approaches may create and support practical training opportunities in food systems and public health going forward.

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)0.008
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.703
GPT teacher head0.528
Teacher spread0.174 · 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

Citations8
Published2018
Admission routes3
Has abstractyes

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