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Record W2565963090 · doi:10.35680/2372-0247.1133

Envisioning mechanisms for success: Evaluation of EBCD at CHEO

2016· article· en· W2565963090 on OpenAlexafffundabout
Kristina Rohde, Mireille Brosseau, Diane Gagnon, Jennifer Schellinck, Christine Kouri

Bibliographic record

VenuePatient Experience Journal · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCarleton UniversityUniversity of OttawaChildren's Hospital of Eastern Ontario
FundersGovernment of CanadaCanadian Foundation for Healthcare Improvement
KeywordsAdaptation (eye)Set (abstract data type)Process (computing)Patient experienceQuality (philosophy)MedicineValue (mathematics)PsychologyMedical educationKnowledge managementNursingComputer scienceHealth carePolitical science

Abstract

fetched live from OpenAlex

To advance patient engagement (PE) and more comprehensively involve patients, families, and staff in quality improvement (QI) at the Children’s Hospital of Eastern Ontario (CHEO), the Experience Based Co-Design (EBCD) approach was piloted. Set against the backdrop of envisioning factors that would facilitate success, an evaluation was designed to assess five domains: strengthening of mutual understanding, collaboration, and partnerships between patients/families and staff; a greater involvement of patients, families, and staff in QI; satisfaction with the process; the ability of EBCD to generate clear and useful data to ascertain the patient/family and staff experience; and the ability of EBCD to generate clear and useful data to improve patient/family and staff experience. The King’s Fund EBCD toolkit was followed to implement the approach. This involved observations and interviews to capture experiences; and feedback events to understand experiences and identify improvement areas. The resulting data was used to evaluate the process relative to the five domains of interest. The evaluation data supported the conclusion that the EBCD process usefully addressed each of the domains of interest, and facilitated PE in QI. In addition, the evaluation revealed important considerations to the success of such an initiative. Using EBCD allows for a more nuanced and comprehensive consultation than traditional methods employed. The research presented here informs the future spread and/or adaptation of EBCD by offering a case for using the approach, but also suggests modifications or considerations to integrate PE methods with existing structures for greater efficiency, success, and value.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.340
GPT teacher head0.498
Teacher spread0.158 · 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 designBench or experimental
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

Citations5
Published2016
Admission routes3
Has abstractyes

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