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Record W3011759598 · doi:10.21037/mhealth.2020.01.05

Videogame intervention to encourage HIV testing and counseling among adolescents

2020· article· en· W3011759598 on OpenAlexaff
Tyra Pendergrass, Kimberly Hieftje, Lindsay R. Duncan, Lynn E. Fiellin

Bibliographic record

VenuemHealth · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsMcGill University
FundersNational Institute on Minority Health and Health DisparitiesNational Institute on Drug AbuseEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood Institute
KeywordsIntervention (counseling)Human immunodeficiency virus (HIV)PsychologyClinical psychologyApplied psychologyMedicineFamily medicinePsychiatry

Abstract

fetched live from OpenAlex

Background: Adolescents in the United States account for one-fifth of new HIV cases, and have the highest rate of undiagnosed HIV, with more than half (51%) not knowing their status. It is a crucial public health concern to help equip youth with the information and autonomy to minimize their risk and know their status. Serious videogames are emerging as valuable tools for health and behavior change in adolescents, and have potential to engage this population and increase their use of HIV testing and counseling (HTC). The purpose of this study was to: (I) modify an original serious game targeting risk reduction and HIV prevention developed by the play2PREVENT Lab and create a new serious game that focuses on HTC; (II) evaluate its feasibility and acceptability; (III) pilot-test the assessment measures that are subsequently being used in a large randomized controlled trial. Methods: Three focus groups with adolescents, aged 14–17 (n=13, mean age =15), informed artwork and storylines for PlayTest! After the game was completed, a pilot test was conducted using a one-group pretest-posttest design to collect data on: (I) participants’ gameplay satisfaction and experience; (II) the validity of the project’s assessments. Twenty-six participants, aged 15–16 were enrolled from a local after-school program. Participants played PlayTest! twice weekly for three weeks. Data were collected on behavior, intentions, knowledge, perceived susceptibility, and attitudes related to HTC at baseline, post-gameplay (three weeks), and follow-up (six weeks). Results: For the focus groups used in the game development, four major themes emerged: (I) adolescents have strong misperceptions about HTC, including who should get tested and what the test entails; (II) adolescents have incorrect knowledge about how HIV is contracted, spread, and treated; (III) adolescents are supportive of their peers getting tested for HIV, but are not likely to get tested themselves; (IV) while the majority of adolescents know where to get tested for HIV, social stigma, misperceptions around HTC, and fear of having a positive diagnosis keep them from seeking it. For the pilot study, overall, participant experience with the game was highly favorable. The assessments were sensitive enough to capture changes in our target variables: intentions (P=0.037) and knowledge (P=0.025) related to HTC at follow-up. Conclusions: The PlayTest! game provides promising results regarding using an engaging and evidence-informed videogame intervention to promote HTC in adolescents.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.053
GPT teacher head0.354
Teacher spread0.301 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations14
Published2020
Admission routes1
Has abstractyes

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