MétaCan
Menu
Back to cohort
Record W4409231189 · doi:10.3138/cam-2025-0204

Sidelined by the side-eye: Exploring the effects of nonverbal communication in healthcare services for 2SLGBTQI+ patients

2025· article· en· W4409231189 on OpenAlexaff
Tara La Rose, Albina Veltman

Bibliographic record

VenueCommunication & Medicine · 2025
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsMcMaster University
Fundersnot available
KeywordsReflexivityHealth careThematic analysisPsychologyNonverbal communicationQualitative researchFocus groupNarrativeQueerUnconscious mindGrounded theoryNursingApplied psychologyMedical educationMedicineDevelopmental psychologySociology

Abstract

fetched live from OpenAlex

Drawing on a subset of the research data from the Queer Queering and Questioning (QQQ) project, a qualitative study examining patient and provider perceptions of good-quality healthcare for people who identify themselves as 2SLGBTQI+ (Two Spirit, Lesbian, Gay, Bisexual, Trans, Queer/Questioning and/or Intersex+), this paper explores the significance of nonverbal communication in shaping healthcare experiences for 2SLGBTQI+ patients. Using data from 68 individual interviews and 11 focus groups, constructivist-grounded theory approaches, including reflexive thematic coding and continuous coding, were used to reveal the effects of unconscious nonverbal communication on patients' experiences. The analysis of the participant narratives suggests that greater attention to communication skills and critical reflexivity in health professional education and training would improve the patient experience by supporting healthcare professionals to manage unconscious responses and by providing professionals with better knowledge and resources to care for the needs of 2SLGBTQI+ patients. Attention to the clinical space and the design of physical environments to demonstrate knowledge, care, and concern for 2SLGBTQI+ patients would also enhance positive outcomes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.406
Teacher spread0.361 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueCommunication & MedicineSame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207