MétaCan
Menu
Back to cohort
Record W3041271080 · doi:10.1080/19371918.2020.1791296

“This Research Is Cool”: Engaging Youth Experiencing Homelessness in Research on Reproductive and Sexual Health

2020· article· en· W3041271080 on OpenAlexaff
Stephanie Begun, A. Algora weber, Joshua Spring, Simran R. A. Arora, Cressida Frey, Alicia Fortin

Bibliographic record

VenueSocial Work in Public Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReproductive healthTabooParticipatory action researchSexual and reproductive health and rightsCommunity-based participatory researchSociologyPsychologyGender studiesCriminologyPublic relationsPolitical scienceReproductive rightsPopulation

Abstract

fetched live from OpenAlex

Youth experiencing homelessness face myriad barriers and inequities regarding their reproductive and sexual health and rights. Moreover, homeless youth are often characterized as "disaffiliated" and depicted as difficult to engage in research. This study qualitatively explored homeless youths' attitudes, beliefs, and needs regarding reproductive and sexual health, and sought their perspectives on being involved in research on such topics, which are often thought of as "taboo" or sensitive. Youth were enthusiastic about openly discussing such issues, which they deemed as highly relevant to their daily lives. Youth identified that how they were engaged in such research, and having opportunities for longer-term contributions to such efforts, were both important and exciting to them. Future social work and public health research efforts should seek to further disrupt narratives of homeless youth as "disaffiliated" and difficult to engage, and in doing so, develop more creative, participatory, and youth-led opportunities for including this group in reproductive and sexual health research.

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)0.038
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0380.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.008
Science and technology studies0.0070.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.007
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 head0.621
GPT teacher head0.575
Teacher spread0.046 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSocial Work in Public HealthSame topicHomelessness and Social IssuesFrench-language works237,207