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Record W2537754364 · doi:10.1080/10538720.2016.1224210

The nature of incoming graduate social work students' attitudes toward sexual minorities

2016· article· en· W2537754364 on OpenAlexaff
Kim D. Jaffee, Adrienne B. Dessel, Michael R. Woodford

Bibliographic record

VenueJournal of Gay & Lesbian Social Services · 2016
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsReligiositySexual orientationLesbianPsychologySocial psychologySexual minorityChurch attendanceSocial dominance orientationSocial workIdeologyBiology and political orientationGender studiesPoliticsDevelopmental psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Competent social work practice with sexual minorities requires educators to identify factors that can be addressed in the graduate social work curriculum to foster affirming attitudes among students. This study explored the attitudes incoming master of social work (MSW) students hold toward sexual minorities, utilizing a scale that addresses contemporary subtle biases toward lesbian, gay, and bisexual (LGB) people, rather than overt, fear- and morality-based objections measured in previous studies. We explored the role of race/ethnicity, age, sex, sexual orientation, religiosity, political ideology, perceived biological causation of sexual orientation, and LGB social contacts on students' attitudes toward sexual minorities. Multivariable linear regression results suggest that being African American/Black (versus White), older, and heterosexual (versus sexual minority), and greater religiosity (importance of religion and frequency of service attendance) and conservative political ideology, predicted less affirming attitudes, while greater endorsement for genetic causation of sexual orientation and exposure to LGB friends and immediate family members each predicted more affirming attitudes among our sample.

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 score0.109
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.064
GPT teacher head0.415
Teacher spread0.352 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

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