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Record W4400103996 · doi:10.1080/26895269.2024.2371417

Social support among gender diverse people: Are we measuring what we think we are?

2024· article· en· W4400103996 on OpenAlexafffundabout
Caitlin Barry, Suraya Meghji, Victoria Jackman, Camden Trepanier, Shannon Coyle, Jill A. Jacobson, Jeremy G. Stewart, Caroline F. Pukall

Bibliographic record

VenueInternational Journal of Transgender Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMemorial University of NewfoundlandQueen's University
FundersQueen's University
KeywordsPsychologySociologyGender studiesSocial psychology

Abstract

fetched live from OpenAlex

Background: To assess social support among gender diverse people, measures normed and validated with cisgender participants are used, most commonly the Multidimensional Scale of Social Support (MSPSS). Despite widespread use, the psychometric properties of the MSPSS have not been systematically investigated among gender diverse people. Thus, it is unclear whether use of the MSPSS is appropriate for this population. Method: = 428). The study was conducted in Canada between 2019 and 2020. Results: After running confirmatory factor analyses on MSPSS items, the factor structure differed from prior studies of cisgender participants. First, support from a significant other did not correlate with support from family or friends, suggesting its inclusion in a global measure of social support may distort findings when all domains are combined. Second although social support from family and friends emerged as two separate domains, qualitative data suggested that the boundaries between these forms of support were unclear. A deductive thematic analysis further highlighted properties of the scale that may not align with gender diverse peoples' experiences. Conclusions: Overall, these findings raise questions about the interpretation of the MSPSS when used with gender diverse people and suggest that an adapted or newly developed measure is needed.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.162
GPT teacher head0.411
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.

The models applied no category: nothing in the taxonomy fit this work.
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 routes3
Has abstractyes

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