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Record W4416349444 · doi:10.2196/82241

Interactive Internet-Based Motivational Interviewing Training for HIV Counseling Support Staff to Improve Health Communication in HIV Care Interactions: Protocol for Training Development and a Pilot Randomized Controlled Trial

2025· article· en· W4416349444 on OpenAlexvenueno aff
Iván C. Balán, Onna Brewer, Bryan A. Kutner, Rebecca Giguere

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMotivational interviewingRandomized controlled trialHuman immunodeficiency virus (HIV)Protocol (science)Communication skills trainingHealth careBehavior changeBehaviour changeTraining (meteorology)

Abstract

fetched live from OpenAlex

BACKGROUND: HIV counseling support staff (CSS) play a crucial role in HIV care outcomes, providing essential access to HIV test counseling, linkage-to-care support, adherence counseling, peer support, and navigation. Effective training in evidence-based interventions like motivational interviewing (MI) is imperative to maximize the impact of CSS in enhancing HIV care outcomes. MI is a collaborative, goal-oriented communication method aimed at bolstering an individual's motivation and movement toward specific goals by eliciting and exploring personal arguments for change. MI has demonstrated efficacy across various medical and mental health outcomes, including its significant impact on the status-neutral HIV Care Continuum. OBJECTIVE: The objective of this study is to develop and pilot an interactive internet-based MI training program for HIV counseling support staff (iMI4HIV), aiming to enhance their communication skills in HIV care interactions. METHODS: The iMI4HIV Study will have 2 phases. In phase 1, Development, we will conduct formative focus groups (FGs) with CSS (n=16) to identify HIV care interactions for demonstration videos and to assess acceptability of gamification components; create MI demonstration videos depicting key interactions from status-neutral HIV Care Continuum; program iMI4HIV (including skills development tasks, gamification components, etc); and pilot a virtual MI (VMI) workshop followed by iMI4HIV (n=8). We will also conduct qualitative interviews with 8 leaders from HIV care agencies to explore their use of existing online MI training programs, acceptability of iMI4HIV, and possible facilitators and impediments to its adoption for training HIV CSS. In phase 2, pilot randomized controlled trial (RCT), we will randomize 30 CSS at a 2:1 ratio (VMI workshop + iMI4HIV vs VMI workshop + waitlist control) to assess the feasibility and acceptability of iMI4HIV and to pilot the RCT processes and assessments for a future efficacy trial. We will also explore the preliminary impact of iMI4HIV on MI skills acquisition. RESULTS: The CSS focus groups began in August 2024, and programming of the training is near completion. We plan to conduct a pilot with 8 participants by the end of 2025, to obtain feedback and conclude phase 1 of the study. The phase 2 pilot RCT is expected to be launched in February 2026, with all data collection to be completed by October 2026. CONCLUSIONS: Our pilot study aims to demonstrate the feasibility and acceptability of an internet-based MI training program for HIV CSS. If successful, this training program has the potential to enhance the delivery of evidence-based interventions in HIV care settings, ultimately improving patient outcomes and adherence to treatment protocols. Further research and larger trials will be needed to confirm these findings and refine the training approach. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/82241.

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)0.021
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.072
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.018
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0090.004
Bibliometrics0.0030.003
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0720.009

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 head0.268
GPT teacher head0.573
Teacher spread0.305 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

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