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Record W2786483680 · doi:10.1186/s40900-018-0086-2

A method for co-creation of an evidence-based patient workbook to address alcohol use when quitting smoking in primary care: a case study

2018· article· en· W2786483680 on OpenAlexafffundabout
Nadia Minian, Aliya Noormohamed, Laurie Zawertailo, Dolly Baliunas, Norman Giesbrecht, Bernard Le Foll, Jürgen Rehm, Andriy V. Samokhvalov, Peter Selby

Bibliographic record

VenueResearch Involvement and Engagement · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversity of TorontoInstitute for Clinical Evaluative SciencesCentre for Addiction and Mental Health
FundersCanadian Cancer Society Research Institute
KeywordsWorkbookPrimary carePsychologyMedicineAlcoholPsychiatryFamily medicine

Abstract

fetched live from OpenAlex

The purpose of this paper is to describe a patient engagement event designed to create an educational workbook with smokers who drink alcohol at harmful levels. The goal was to create a workbook that combined scientific evidence with patients’ values, preferences, and needs. Fourteen adult smokers who drink alcohol were invited to the Centre for Addiction and Mental Health (CAMH) to take part in a four-hour event to help design the workbook with the CAMH research team. Participants provided their opinions and ideas to create an outline for the workbook, including activities, images, and titles. The workbook – called Self-Awareness – is currently being offered in a smoking cessation program in 221 primary care clinics across Ontario to help smokers quit or reduce their harmful alcohol use. The patient engagement event was a useful way to co-create educational materials that incorporate both scientific research and patient needs. Background Evidence-based medicine is the integration of best research evidence with clinical expertise and patient values. There are few methodologies on how to design evidence-based programs and resources to include patient values. The latter is an important aspect of patient-centered care, and is essential for patients to trust the recommendations and empower them as consumers to make informed choices. This manuscript describes a participatory research approach to design patient-facing educational materials that incorporate both evidence-based and community-sensitive principles. These materials are intended to support smokers to reduce or stop harmful alcohol consumption. Methods Adult smokers who report consuming alcohol were invited to a co-creation meeting at the Centre for Addiction and Mental Health’s Nicotine Dependence Service to guide the adaptation of evidence-based materials. The four-hour event consisted of individual reflections, group discussions, and consensus-building interactions. Detailed notes were taken and then incorporated into the material. Results Fourteen individuals participated in the event. The end product was a descriptive outline of an educational resource – entitled Self-Awareness – incorporating material from evidence-based workbooks and patient-driven features. Participants collaboratively selected the resource’s content, structure, and titles. Conclusions This model describes a participatory research method that emphasizes the value of the patient perspective; preliminary evidence finds this adaptation approach can increase the adoption of resources. The process described in this article could be replicated in other settings to co-create evidence-based resources, interventions, and programs that reflect the needs of the community. Trial registration ClinicalTrials.gov NCT03108144 . Retrospectively registered 11 April 2017.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.505
GPT teacher head0.587
Teacher spread0.083 · 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

Citations13
Published2018
Admission routes3
Has abstractyes

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