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Record W4407143221 · doi:10.1353/cpr.2024.a948676

“We Need To Be at the Table”: Collaboration with Lived Experts

2024· article· en· W4407143221 on OpenAlexaboutno aff
Sarah L. Canham, Rachel Weldrick, Anne Cartledge, Hilary Chapple, Chris Danielsen, Dorothy Kestle, M. Gauthier, Samantha Teichman

Bibliographic record

VenueProgress in community health partnerships · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)Asset (computer security)Public relationsSociologyPsychologyPolitical scienceMedical educationMedicineGender studies

Abstract

fetched live from OpenAlex

BACKGROUND: The recognition of lived experience as an asset has led to increased involvement of individuals most affected by social or medical conditions in research. OBJECTIVES: This paper presents an example of a LE advisory group that co-conceptualized and executed a knowledge mobilization project on aging and homelessness within three Canadian cities (Vancouver, Calgary, and Montreal). METHODS: We established the advisory group, determined the group's priorities and objectives, and fostered community engagement through webinars and in-person events. LESSONS LEARNED: We learned the importance of digital support to enable inclusion of advisors with experiences of homelessness, providing honoraria to for advisors' time and contributions, scheduling meetings on the same day and time each month, and dedicating meeting time for advisors' personal updates and experiences. CONCLUSIONS: This model can be replicated by other research teams studying homelessness, aging, or similar marginalized groups, enhancing the impact of research and knowledge mobilization efforts.

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)0.004
metaresearch head score (Gemma)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.253
GPT teacher head0.502
Teacher spread0.249 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

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