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Record W6990346979

Developing an online health literacy curriculum for two German universities: a key stakeholder approach

2018· article· en· W6990346979 on OpenAlexaboutno aff

Bibliographic record

VenueKölner Universitäts PublikationsServer (Universität zu Köln) · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsHealth literacyGermanFocus groupCurriculumInformation literacyStakeholderBlueprintLiteracyResource (disambiguation)
DOInot available

Abstract

fetched live from OpenAlex

Health literacy is a significant resource for daily life in society. Global evidence reveals that there are less than ideal levels of health literacy in populations. One potential straproviding them with the skills and tools that will improve their knowledge and practice as our future workforce. The purpose of this study was to articulate the need to develop an online health literacy introductory course for university students in Germany. A total of 16 students from two German universities participated in focus group interviews to collect data on the extent of student health literacy awareness and related health and education needs. Nine international stakeholders participated in an online self-guided review of a comprehensive draft course to obtain detailed feedback from experts in the education and health literacy fields. Results revealed that both focus group and international stakeholders are in support of developing an online health literacy curriculum. To build the draft curriculum, an existing Canadian health literacy online course was adapted as a blueprint for the German context. The proposed course was customized based on the findings from the focus groups and international stakeholder feedback, which is intended to help inform and determine contents, design, and delivery of such a course applicable for universities in Germany and beyond.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0060.000
Scholarly communication0.0000.029
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.151
GPT teacher head0.447
Teacher spread0.296 · 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 designTheoretical or conceptual
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

Citations0
Published2018
Admission routes1
Has abstractyes

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