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Record W2905523720 · doi:10.12927/hcq.2018.25636

Evaluating Patient, Family and Public Engagement in Health Services Improvement and System Redesign

2018· article· en· W2905523720 on OpenAlexaff
Julia Abelson, Anya Humphrey, Ania Syrowatka, Julia Bidonde, Maria Judd

Bibliographic record

VenueHealthcare Quarterly · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCanadian Foundation for Healthcare Improvement
Fundersnot available
KeywordsPublic healthPublic engagementPublic relationsKey (lock)Process managementBusinessHealth servicesService (business)Best practiceKnowledge managementMedicineNursingPolitical scienceMarketingComputer scienceEnvironmental healthComputer security

Abstract

fetched live from OpenAlex

As efforts to actively involve patients, family members and the broader public in health service improvement and system redesign have grown, increasing attention has also been paid to evaluation of their engagement in the health system. We discuss key concepts and approaches related to evaluation, drawing particular attention to different and potentially competing goals, stakeholders and epistemological entry points. Evaluation itself can be supported by an increasing number of frameworks and tools, matched to the relevant purpose and approach. The patient engagement evaluation field faces several challenges, including the need for greater specification of both the form and the context of engagement, the need to balance the measurement imperative with the relational aspects of care and the need for supportive organizations with the capacity and commitment to undertake high-quality engagement and its evaluation.

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)0.231
metaresearch head score (Gemma)0.253
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.948

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2310.253
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0050.006
Scholarly communication0.0080.006
Open science0.0010.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.256
GPT teacher head0.454
Teacher spread0.197 · 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.

Study designQualitative
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

Citations53
Published2018
Admission routes1
Has abstractyes

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