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#SafeHandsSafeHearts intervention.

2024· article· en· W6961255747 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldComputer Science
TopicGraph Theory and Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)AnxietyTransgenderDistressPublic healthMen who have sex with menTransgender womenHealth equitySexual minority

Abstract

fetched live from OpenAlex

<div> Purpose Sexual and gender minority and racialized populations experienced heightened vulnerability during the Covid-19 pandemic. Marginalization due to structural homophobia, transphobia and racism, and resulting adverse social determinants of health that contribute to health disparities among these populations, were exacerbated by the Covid-19 pandemic and public health measures to control it. We developed and tested a tailored online intervention (#SafeHandsSafeHearts) to support racialized lesbian, gay, bisexual, transgender, queer, and other persons outside of heteronormative and cisgender identities (LGBTQ+) in Toronto, Canada during the pandemic. Methods We used a quasi-experimental pre-test post-test design to evaluate the effectiveness of a 3-session, peer-delivered eHealth intervention in reducing psychological distress and increasing Covid-19 knowledge and protective behaviors. Individuals ≥18-years-old, resident in Toronto, and self-identified as sexual or gender minority were recruited online. Depressive and anxiety symptoms, and Covid-19 knowledge and protective behaviors were assessed at baseline, 2-weeks postintervention, and 2-months follow-up. We used generalized estimating equations and zero-truncated Poisson models to evaluate the effectiveness of the intervention on the four primary outcomes. Results From March to November 2021, 202 participants (median age, 27 years [Interquartile range: 23–32]) were enrolled in #SafeHandsSafeHearts. Over half (54.5%, n = 110) identified as cisgender lesbian or bisexual women or women who have sex with women, 26.2% (n = 53) cisgender gay or bisexual men or men who have sex with men, and 19.3% (n = 39) transgender or nonbinary individuals. The majority (75.7%, n = 143) were Black and other racialized individuals. The intervention led to statistically significant reductions in the prevalence of clinically significant depressive (25.4% reduction, p < .01) and anxiety symptoms (16.6% reduction, p < .05), and increases in Covid-19 protective behaviors (4.9% increase, p < .05), from baseline to postintervention. Conclusion We demonstrated the effectiveness of a brief, peer-delivered eHealth intervention for racialized LGBTQ+ communities in reducing psychological distress and increasing protective behaviors amid the Covid-19 pandemic. Implementation through community-based organizations by trained peer counselors supports feasibility, acceptability, and the importance of engaging racialized LGBTQ+ communities in pandemic response preparedness. This trial is registered with ClinicalTrials.gov, number NCT04870723. </div>

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0570.009

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 head0.019
GPT teacher head0.255
Teacher spread0.237 · 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