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Record W4389625234 · doi:10.1353/cpr.2023.a914120

Lessons Learned Developing Client Navigation for People who are Trans and Gender Diverse

2023· article· en· W4389625234 on OpenAlexaboutno aff
Alana Cattapan, Stéphanie J. Madill, Megan Clark, James Young, Cat Haines, Lori Ebbeson

Bibliographic record

VenueProgress in community health partnerships · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careParticipatory action researchMultidisciplinary approachCommunity-based participatory researchMultidisciplinary teamCitizen journalismPublic relationsParticipatory designNursingMedical educationPsychologyMedicinePolitical scienceSociologyEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: People who are trans and gender diverse (PTGD) are underserved regarding healthcare in Canada, including the province of Saskatchewan. OBJECTIVES: Design and conduct a research project that will address immediate and pressing community-identified needs related to improving access to healthcare for PTGD in Saskatchewan. METHODS: A multidisciplinary, community-based collaboration was established to address the self-identified obstacles to accessing healthcare of PTGD in Saskatchewan. This resulted in a pilot study creating and evaluating a healthcare navigation program. LESSONS LEARNED: The project led to four key lessons: 1) prioritizing team building and the well-being of team members; 2) committing to community-based participatory approaches from the outset; 3) taking language seriously; and 4) acknowledging and addressing power imbalances in our team. CONCLUSIONS: The lessons we learned have enabled us to sustain a large, diverse, research team that centers the experience of PTGD in Saskatchewan and is responsive to community need.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.513
GPT teacher head0.536
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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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