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SCI-U: E-learning for patient education in spinal cord injury rehabilitation

2012· article· en· W2006804562 on OpenAlexafffundabout
John D. Shepherd, Karla M. Badger-Brown, Matthew Legassic, Saagar Walia, Dalton L. Wolfe

Bibliographic record

VenueJournal of Spinal Cord Medicine · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsLawson Health Research InstituteToronto Rehabilitation InstituteUniversity Health Network
FundersRick Hansen InstituteToronto Rehabilitation InstituteLawson Health Research InstituteAlberta Health Services
KeywordsRehabilitationProcess (computing)Medical educationResource (disambiguation)Work (physics)MedicineFocus groupPatient educationSpinal cord injuryMultimediaComputer sciencePhysical therapySpinal cordNursingBusinessEngineering

Abstract

fetched live from OpenAlex

BACKGROUND/OBJECTIVES: To develop an online patient education resource for use in spinal cord injury rehabilitation. PARTICIPANTS: The development process involved more than 100 subject-matter experts (SMEs) (rehabilitation professionals and consumers) from across Canada. Preliminary evaluation was conducted with 25 end-users. METHODS: An iterative development process was coordinated by a project team; SMEs (including patients) developed the content in working groups using wiki-based tools. Multiple rounds of feedback based on early prototypes helped improve the courses during development. RESULTS: Five courses were created, each featuring more than 45 minutes of video content and hundreds of media assets. Preliminary evaluation results indicate that users were satisfied by the courses and perceived them to be effective. CONCLUSIONS: This is an effective process for developing multimedia patient education resources; the involvement of patients in all parts of the process was particularly helpful. Future work will focus on implementation, integration into clinical practice and other delivery formats (smart phones, tablets).

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)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.064
GPT teacher head0.519
Teacher spread0.455 · 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 designObservational
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

Citations40
Published2012
Admission routes3
Has abstractyes

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