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Record W3027474795 · doi:10.1177/2374373520925721

Evaluation of Patient Engagement in a Clinical Emergency Care Network: Findings From the BC Emergency Medicine Network

2020· article· en· W3027474795 on OpenAlexaff
Sharla Drebit, Kim Eggers, Chantel Archibald, Riyad B. Abu‐Laban, Kendall Ho, Afshin Khazei, Ronald R. Lindstrom, Julian Marsden, Ed. Martin, Jim Christenson

Bibliographic record

VenueJournal of Patient Experience · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsExperiential learningMedical emergencyPatient careCollaborative networkPatient experienceExperiential knowledgeMedicinePublic engagementMedical educationNursingKnowledge managementBusinessPsychologyPublic relationsHealth carePolitical scienceComputer science

Abstract

fetched live from OpenAlex

The British Columbia Emergency Medicine Network (EM Network) has collaborated with patient partners to utilize their experiential knowledge to inform planning and implementation. Patient partners participated in several EM Network committees and initiatives. This study evaluated how patient partners and other leaders in the EM Network perceived patient engagement efforts 1 year after launch. The Public and Patient Engagement Evaluation Tool V2.0 found that there was an appropriate level of patient engagement at this early stage, an opportunity to attract more patient partners as the EM Network grows, and a need to ensure adequate resources to support more activities.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.410
GPT teacher head0.516
Teacher spread0.105 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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