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Record W2144841977 · doi:10.32920/ryerson.14668857.v1

Designing a cardiovascular surgical web-based patient education intervention: A discussion paper.

2021· article· en· W2144841977 on OpenAlexaff
Suzanne Fredericks, Géraldine Martorella, Erone Newman, B. Swart

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsSt. Michael's HospitalUniversité de MontréalToronto Metropolitan University
Fundersnot available
KeywordsIntervention (counseling)Psychological interventionPatient educationMedicineVisibilityControl (management)Complement (music)Web applicationPopulationMedical educationPsychologyNursingComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The effectiveness of written education materials have been examined across the cardiovascular surgical setting. Inconsistent findings have indicated minimal changes to patient outcomes. The absence of significant findings may be due to the lack of control patients have over the frequency in which they can access information, the amount and type of content they are able to peruse, and the actual time the materials can be reviewed. A complement to in-hospital patient educational interventions is web-based patient education, accessed during the home discharge period. This discursive paper presents a summary of a planned web-based patient education intervention that has been designed for use by a predominantly elderly population. In particular, modifications to account for reduced visibility, decreased hearing, and onset of physical impairment are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.404
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations1
Published2021
Admission routes1
Has abstractyes

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