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Record W3147741236 · doi:10.1080/01900692.2021.1903500

A COMPASS for Navigating Relationships in Co-Production Processes Involving Vulnerable Populations

2021· article· en· W3147741236 on OpenAlexaffabout
Gillian Mulvale, Ashleigh Miatello, Jenn Green, Maxwell Tran, Christina Roussakis, Alison Mulvale

Bibliographic record

VenueInternational Journal of Public Administration · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsCanadian Mental Health AssociationUniversity of TorontoMcMaster University
Fundersnot available
KeywordsVulnerability (computing)StakeholderCompassProduction (economics)Power (physics)Mental healthPsychologySociologyPublic relationsPolitical scienceComputer scienceGeographyCartographyComputer securityPsychotherapist

Abstract

fetched live from OpenAlex

When it comes to engaging vulnerable populations in co-production, power imbalances across stakeholder groups can create methodological challenges. A thoughtful, planned, and responsive approach is needed to prepare vulnerable participants to fully engage in co-production processes. Data from key informant interviews (n = 16) and author reflections on three Experience-based co-design (EBCD) studies involving youth (16–25 years) with mental health issues in Ontario Canada, were analyzed. Four overarching themes and 12 subthemes were identified, and heuristic tools (a relational COMPASS and MAPS directions) were developed to assist researchers in navigating vulnerability, power and relational issues in co-production processes involving vulnerable populations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.143
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.969
Threshold uncertainty score0.756

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1430.112
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0310.090
Scholarly communication0.0340.037
Open science0.0050.030
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0050.001

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 head0.501
GPT teacher head0.524
Teacher spread0.023 · 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 source (direct Gemma or distilled Codex), 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

Citations34
Published2021
Admission routes2
Has abstractyes

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