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Record W4308254087 · doi:10.1007/s10461-022-03876-2

Integrating Adolescent Mental Health into HIV Prevention and Treatment Programs: Can Implementation Science Pave the Path Forward?

2022· article· en· W4308254087 on OpenAlexaff
Judith Boshe, Veronica Brtek, Kristin Beima‐Sofie, Paula Braitstein, Merrian J. Brooks, Julie A. Denison, Geri R. Donenberg, Elizabeth Kemigisha, Peter Memiah, Irene Njuguna, Ohemaa B. Poku, Sarah T. Roberts, Aisa Shayo, Dorothy E. Dow

Bibliographic record

VenueAIDS and Behavior · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational Institute of Mental HealthNational Institutes of Health
KeywordsHealth psychologyMental healthAllianceReferralHuman immunodeficiency virus (HIV)Public healthImplementation researchMedicineNursingPsychologyMedical educationFamily medicinePsychiatryPolitical sciencePsychological intervention

Abstract

fetched live from OpenAlex

Adolescent mental health (AMH) is a critical driver of HIV outcomes, but is often overlooked in HIV research and programming. The implementation science Exploration, Preparation, Implementation, Sustainment (EPIS) framework informed development of a questionnaire that was sent to a global alliance of adolescent HIV researchers, providers, and implementors working in sub-Saharan Africa with the aim to (1) describe current AMH outcomes incorporated into HIV research within the alliance; (2) identify determinants (barriers/gaps) of integrating AMH into HIV research and care; and (3) describe current AMH screening and referral systems in adolescent HIV programs in sub-Saharan Africa. Respondents reported on fourteen named studies that included AMH outcomes in HIV research. Barriers to AMH integration in HIV research and care programs were explored with suggested implementation science strategies to achieve the goal of integrated and sustained mental health services within adolescent HIV programs.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.092
GPT teacher head0.474
Teacher spread0.382 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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