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Record W2913365189 · doi:10.1097/nne.0000000000000646

Creation and Online Use of Patient-Centered Videos, Digital Storytelling, and Interactive Self-testing Questions for Teaching Pathophysiology

2019· article· en· W2913365189 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNurse Educator · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsToronto Public HealthCentre for Addiction and Mental Health
Fundersnot available
KeywordsSummative assessmentMedical educationMedicineInteractivityStorytellingDigital storytellingPsychologyComputer scienceMultimediaFormative assessmentPedagogyNarrative

Abstract

fetched live from OpenAlex

BACKGROUND: Nursing students need to not only understand the pathophysiological basis of disease but also acquire insight into its effects on patients and their families. PURPOSE: Digital storytelling was used to engage students in self-directed, online learning, allowing them to identify with patients dealing with disease and its consequences. METHODS: Scripts were written and videos created that simulated patient experiences with select diseases of the gastrointestinal and respiratory systems as well as diabetes. Videos plus online self-testing questions were provided to nursing students studying pathophysiology and student outcomes on summative examinations compared before and after introduction of the videos. RESULTS: Students had improved outcomes on summative examination questions that targeted diseases addressed in the video modules. CONCLUSIONS: Digital storytelling is an effective way to portray illness from a patient perspective, and the addition of this approach to pathophysiology instruction can benefit student learning.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.370
Teacher spread0.334 · 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