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Record W3153108741 · doi:10.1177/23743735211008300

Engaging Women With Lived Experience: A Novel Cross-Canada Approach

2021· article· en· W3153108741 on OpenAlexaffabout
Moira Teed, Julia Ianiro, Cynthia Culhane, Jennifer Monaghan, Judit Takács, Gavin Arthur, Amanda Nash

Bibliographic record

VenueJournal of Patient Experience · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsHeart and Stroke Foundation
Fundersnot available
KeywordsPublic engagementLived experiencePsychologySession (web analytics)ConfidentialityGerontologyMedical educationNursingMedicinePublic relationsPolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

Women with heart disease, stroke, and vascular cognitive impairment (VCI) experience gender inequities across the health care continuum. The Heart and Stroke Foundation of Canada conducted needs assessment to inform its approach in addressing health inequities experienced by women with heart disease, stroke, and VCI across the continuum of care. Although specific input is confidential, this article outlines the engagement methods used and the evaluation results. The 3-stage engagement process consisted of an internal content review, 18 in-person discussion groups via a cross-Canada tour, 14 expert interviews, and a collaboration session. In total, 204 and 57 participants were recruited for the cross-Canada tour and collaboration session, respectively. Using the Public and Patient Engagement Evaluation Tool, participants scored the engagement processes positively and found participation to be a valuable use of their time. This undertaking highlighted aspects to consider when engaging people with lived experience and how engagement can support the recovery journey. Insights presented throughout this article can help inform future research that seeks to engage stakeholders at a national level.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.400
GPT teacher head0.592
Teacher spread0.192 · 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.

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

Citations1
Published2021
Admission routes2
Has abstractyes

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