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Record W4416588298 · doi:10.2196/79286

A Blended Educational Program to Promote Dialogue on Patient Safety Between Patient and Family Advisory Councils and Health Care Organizations: Codevelopment Study

2025· article· en· W4416588298 on OpenAlexvenueno aff
Yannick Blum, Larissa Brust, Matthias Weigl, Qëndresa Rramani

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipPatient safetyHealth careEducational programProgram evaluationProgram Design LanguageSafety culture

Abstract

fetched live from OpenAlex

Background: Reducing patient harm and improving patient safety is a central objective in global health care. Effective communication and meaningful patient engagement are considered essential strategies to achieve this goal. However, implementation of structured and strategic patient engagement at the organizational level remains limited, particularly in the context of patient safety. Patient and family advisory councils (PFACs) offer a promising model to enhance organizational-level patient engagement, yet guidance on implementation and targeted training for PFAC members is scarce. Objective: This study aimed to codevelop an evidence-informed, blended educational program designed to strengthen PFAC members' competencies in patient safety and communication, and to foster strategic collaboration between PFACs and health care organizations. Methods: The intervention was systematically developed using a logic model framework that structures the development process from available and required resources to the ultimate objectives and impacts. The primary target group included PFAC members, such as patients, relatives, and advocates, as well as health care representatives in leadership, quality management, or coordination roles. The program's content and structure were informed by a nationwide needs and requirements analysis among PFAC members, conducted using a mixed methods Delphi approach, and by a rapid scoping review on existing educational resources and evidence on PFAC engagement in patient safety. Results: Our Partners for Patient Safety blended educational program consisted of 2 modular components: a self-paced e-learning module and a subsequent on-site workshop module. Content addressed three core topics: (1) fundamentals of patient safety, (2) engagement of PFACs, and (3) communication and collaborative goal setting. The e-learning module provided theoretical knowledge using diverse didactic formats, such as interactive tasks, videos, and downloadable materials, and included applied examples using established decision-making and goal-setting frameworks. The workshop module built on the e-learning module and facilitated local implementation through collaborative exercises focused on stakeholder perspectives, communication barriers, and joint goal development. Both modules were aligned with defined learning objectives and combined passive and active learning strategies to promote engagement and practical application. Conclusions: The Partners for Patient Safety program seeks to develop PFAC members' competencies, promote collaboration in patient safety, and foster a culture of safety and partnership within health care organizations. By combining theoretical knowledge with practical, collaborative learning, the program addresses key barriers to effective PFAC engagement at the organizational level. Its modular design allows flexible implementation and has the potential to strengthen cooperation between PFACs and health care representatives, ultimately improving patient safety outcomes. Further evaluation of the program's implementation and effectiveness is needed.

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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.069
GPT teacher head0.512
Teacher spread0.443 · 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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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