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‘I told him not to use condoms’: masculinities, femininities and sexual health of Aboriginal Canadian young people

2010· article· en· W1556704192 on OpenAlexfundaboutno aff
Karen Devries, Caroline Free

Bibliographic record

VenueSociology of Health & Illness · 2010
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsCondomReproductive healthPsychologyIntervention (counseling)Psychological interventionYoung adultGender studiesNegotiationDevelopmental psychologySocial psychologyPopulationDemographySociologyMedicineHuman immunodeficiency virus (HIV)Psychiatry

Abstract

fetched live from OpenAlex

Gendered power imbalances in heterosexual relationships are a key target of gender-sensitive STI risk reduction interventions. Gendered aspects of sexual behaviour have not been explored among Canadian indigenous young people, who are at elevated risk for STI relative to other young Canadians. We used data from in-depth qualitative interviews with 15 male and 15 female indigenous young people to explore gendered sexual behaviour and its implications for STI reduction. There was a pervasive 'double standard' where young men were expected to be sexually aggressive and young women were expected to resist sexual advances; but we also observed 'alternative' or non-hegemonic behaviours. Specifically, young women were often very active participants in sexual negotiations, could refuse condom use and sometimes pressured their male partners to not use condoms. Young men also described being the object of coerced sex, and did not always perceive female sexual desire in negative terms, and were not always receptive to sex. The gendered sexual attitudes and behaviours in our sample were much more complex than usually described in the literature. Intervention work needs to take more realistic account of the sexual interactions that occur between young people.

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 categoriesMeta-epidemiology (narrow), Science 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 score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.089
GPT teacher head0.435
Teacher spread0.346 · 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

Citations46
Published2010
Admission routes2
Has abstractyes

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