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Record W3088613857 · doi:10.1186/s40900-020-00232-3

“They heard our voice!” patient engagement councils in community-based primary care practices: a participatory action research pilot study

2020· article· en· W3088613857 on OpenAlexafffundabout
Julie Haesebaert, Isabelle Samson, Hélène Lee-Gosselin, Sabrina Guay-Bélanger, Jean-François Proteau, Guy Drouin, Chantal Guímont, Luc Vigneault, Annie Poirier, Priscille-Nice Sanon, Geneviève Roch, Marie-Ève Poitras, Annie LeBlanc, France Légaré

Bibliographic record

VenueResearch Involvement and Engagement · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité de SherbrookeUniversité du Québec à ChicoutimiCentre Jeunesse de QuebecInstitut Universitaire en Santé Mentale de QuébecUniversité LavalCentre hospitalier universitaire de QuébecCentres Intégré Universitaires de Santé et de Services SociauxHôpital Saint-François d'AssiseCanadian Patient Safety InstituteCentre intégré universitaire de santé et de services sociaux de la Capitale-Nationale
FundersCanadian Institutes of Health ResearchUniversité Laval
KeywordsParticipatory action researchAction (physics)Citizen journalismPrimary careCommunity-based participatory researchAction researchCommunity engagementCommunity participationNursingPublic relationsMedical educationPsychologyMedicineSociologyPolitical sciencePedagogyFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Patient engagement could improve the quality of primary care practices. However, we know little about effective patient engagement strategies. We aimed to assess the acceptability and feasibility of embedding advisory councils of clinicians, managers, patients and caregivers to conduct patient-oriented quality improvement projects in primary care practices. METHODS: Using a participatory action research approach, we conducted our study in two non-academic primary care practices in Quebec City (Canada). Patient-experts (patients trained in research) were involved in study design, council recruitment and meeting facilitation. Advisory councils were each to include patients and/or caregivers, clinicians and managers. Over six meetings, councils would identify quality improvement priorities and plan projects accordingly. We assessed acceptability and feasibility of the councils using non-participant observations, audio-recordings and self-administered questionnaires. We used descriptive analyses, triangulated qualitative data and performed inductive thematic analysis. RESULTS: Between December 2017 and June 2018, two advisory councils were formed, each with 11 patients (36% male, mean age 53.8 years), a nurse and a manager practising as a family physician (25% male, mean age 45 years). The six meetings per practice occurred within the study period with a mean of eight patients per meeting. Councils worked on two projects each: the first council on a new information leaflet about clinic organization and operation, and on communications about local public health programs; the second on methods to further engage patients in the practice, and on improving the appointment scheduling system. Median patient satisfaction was 8/10, and 66.7% perceived councils had an impact on practice operations. They considered involvement of a manager, facilitation by patient-experts, and the fostering of mutual respect as key to this impact. Clinicians and managers liked having patients as facilitators and the respect among members. Limiting factors were difficulty focusing on a single feasible project and time constraints. Managers in both practices were committed to pursuing the councils post-study. CONCLUSION: Our results indicated that embedding advisory councils of clinicians, managers, patients and caregivers to conduct patient-oriented quality improvement projects in primary care practices is both acceptable and feasible. Future research should assess its transferability to other clinical contexts.

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)0.057
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0570.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.009
Insufficient payload (model declined to judge)0.0000.001

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 head0.927
GPT teacher head0.613
Teacher spread0.314 · 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 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

Citations32
Published2020
Admission routes3
Has abstractyes

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