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Record W3045775063 · doi:10.2196/17154

eHealth Communication With Clients at Community-Based HIV/AIDS Service Organizations in the Southern United States: Cross-Sectional Survey

2020· article· en· W3045775063 on OpenAlexvenueno aff
Lisa T. Wigfall

Bibliographic record

VenueJMIR Formative Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsSnowball samplingeHealthMedicineService (business)VideoconferencingFamily medicineNursingBusinessHealth carePolitical scienceTelecommunicationsEngineeringMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Providing HIV/STD testing and prevention education, medical and nonmedical case management, housing assistance, transportation services, and patient navigation are just a few examples of how community-based HIV/AIDS service organizations will help the United States realize the goals of the updated National HIV/AIDS Strategy. OBJECTIVE: In this study, the aim was to assess electronic data security confidence level, electronic communication behaviors, and interest in using eHealth communication tools with clients of staff at community-based HIV/AIDS service organizations. METHODS: Staff were recruited from 7 community-based HIV/AIDS service organizations in the southern United States (3 in South Carolina and 4 in Texas). The principal investigator used state department of health websites to identify community-based HIV/AIDS service organizations. Staff were included if they provided HIV/STD prevention education to clients. A recruitment letter was sent to community-based HIV/AIDS service organization leaders who then used snowball sampling to recruit eligible staff. Chi-square tests were used. RESULTS: Among staff (n=59) who participated in the study, 66% (39/59) were very or completely confident that safeguards are in place to keep electronically shared information from being seen by other people; 68% (40/59) used email, 58% (34/59) used text messages, 25% (15/59) used social media, 15% (9/59) used a mobile app, 8% (5/59) used web-enabled videoconferencing, and 3% (2/59) used other tools (eg, electronic medical record, healthnavigator.com website) to communicate electronically with their clients. More than half were very interested in using eHealth communication tools in the future for sharing appointment reminders (67%, 38/59) and general health tips (61%, 34/59) with their clients. Half were very interested in using eHealth communication tools in the future to share HIV medication reminders with their clients (50%, 29/59). Forty percent (23/59) were very interested in using eHealth communication tools to share vaccination reminders with their clients. CONCLUSIONS: Community-based HIV/AIDS service organization staff had some level of confidence that safeguards were in place to keep electronically shared information from being seen by other people. This is critically important given the sensitivity of the information shared between community-based HIV/AIDS service organization staff and their clients, and because many staff were very interested in using eHealth communication tools with their clients in the future. It is very likely that eHealth communication tools can be used in community settings to improve health outcomes across the HIV care continuum; in the interim, more research is needed to better understand factors that may facilitate or impede community-based HIV/AIDS service organization staff use and client acceptability.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.204
GPT teacher head0.520
Teacher spread0.316 · 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 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

Citations2
Published2020
Admission routes1
Has abstractyes

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