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Record W2582802404 · doi:10.1108/lhs-07-2016-0032

The new frontier of public health education

2017· article· en· W2582802404 on OpenAlexaff
David Birnbaum, Kathryn Gretsinger, Ursula Ellis

Bibliographic record

VenueLeadership in health services · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British ColumbiaInstitute of Population and Public Health
Fundersnot available
KeywordsFrontierPublic healthPublic relationsPolitical scienceMedicineEconomic growthPublic administrationPsychologyNursingEconomics

Abstract

fetched live from OpenAlex

Purpose The aim of this paper is to describe the experience and educational benefits of a course that has several unique educational design features. Design/methodology/approach This includes narrative description of faculty and student experience from participants in a flipped-instructional-design inter-professional education course. Findings "Improving Public Health - An Interprofessional Approach to Designing and Implementing Effective Interventions" is an undergraduate public health course open to students regardless of background. Its student activities mirror the real-life tasks and challenges of working in a public health agency, including team-building and leadership; problem and project definition and prioritization; evidence-finding and critical appraisal; written and oral presentation; and press interviews. Students successfully developed project proposals to address real problems in a wide range of communities and settings and refined those proposals through interaction with professionals from population and public health, journalism and library sciences. Practical implications Undergraduate public health education is a relatively new endeavor, and experience with this new approach may be of value to other educators. Originality/value Students in this course, journalism graduate students who conducted mock interviews with them and instructors who oversaw the course all describe unique aspects and related personal benefit from this novel approach.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.204
GPT teacher head0.421
Teacher spread0.217 · 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 designOther design
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
Published2017
Admission routes1
Has abstractyes

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