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Record W2896932089 · doi:10.1186/s40900-018-0115-1

People with lived experience (PWLE) of depression: describing and reflecting on an explicit patient engagement process within depression research priority setting in Alberta, Canada

2018· article· en· W2896932089 on OpenAlexafffundabout
Lorraine Breault, Katherine Rittenbach, Kelly Hartle, Robbie Babins‐Wagner, Catherine de Beaudrap, Yamile Jasaui, Emily Ardell, Scot E. Purdon, Ashton Michael, Ginger Sullivan, A Unger, Lorin Vandall-Walker, Brad Necyk, Kiara Krawec, Elizabeth Manafò, Ping Mason-Lai

Bibliographic record

VenueResearch Involvement and Engagement · 2018
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta InnovatesAthabasca UniversityAlberta Health Services
KeywordsDepression (economics)Process (computing)PsychologyLived experiencePsychotherapistComputer science

Abstract

fetched live from OpenAlex

The Alberta Depression Research Priority Setting Project aimed to meaningfully involve patients, families and clinicians in determining a research agenda aligned to the needs of Albertans who have experienced depression. The project was modeled after a process developed in the UK by the James Lind Alliance and adapted to fit the Alberta, Canada context. This study describes the processes used to ensure the voices of people with lived experience of depression were integrated throughout the project stages. The year long project culminated with a facilitated session to identify the top essential areas of depression research focus. People with lived experience were engaged as part of the project’s Steering Committee, as survey participants and as workshop participants. It is hoped this process will guide future priority setting opportunities and advance depression research in Alberta. Background The Depression Research Priority Setting (DRPS) project has the clear aim of describing the patient engagement process used to identify depression research priorities and to reflect on the successes of this engagement approach, positive impacts and opportunities for improvement. To help support patient-oriented depression research priority setting in Alberta, the Patient Engagement (PE) Platform of the Alberta Strategy for Patient Oriented Research Support for People and Patient-Oriented Research and Trials (SUPPORT) Unit designed, along with the support of their partners in addictions and mental health, an explit process to engage patients in the design and execution of the DRPS. Methods The UK’s James Lind Alliance (JLA) Priority Setting Partnership (PSP) method was adapted into a six step process to ensure voices of “people with lived experience” (PWLE) with depression were included throughout the project stages. This study uses an explicit and parallel patient engagement process throughout each estage of the PSP designed by the PE Platform. Patient engagement was divided into a five step process: i) Awareness and relationship building; ii) Co-designing and co-developing a shared decision making process; iii) Collaborative communication; iv) Collective sensemaking; and v) Acknowledgement, celebration and recognition. A formative evaluation of the six PE processes was undertaken to explore the success of the parallel patient engagement process. Results This project was successful in engaging people with lived depression experience as partners in research priority setting, incorporating their voices into the discussions and decisions that led to the top 25 depression research questions. Conclusions The DRPS project has positively contributed to depression research in Canada by identifying the priorities of Albertans who have experienced depression for depression research. Dissemination activities to promote further knowledge exchange of prioritized research questions, with emphasis on the importance of process in engaging the voices of PWLE of depression are planned.

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)0.015
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.644
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
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 head0.566
GPT teacher head0.533
Teacher spread0.033 · 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

Citations27
Published2018
Admission routes3
Has abstractyes

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