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Record W4416791218 · doi:10.2196/68561

Evaluation of a Pilot mHealth Intervention to Engage Primary Care Clients at an Urban Clinic Serving Marginalized Populations: Mixed-Methods Cohort Study

2025· article· en· W4416791218 on OpenAlexaffvenueabout
Lauren Harrison, Antonio Marante Changir, Adedayo Tunde Ajidahun, C. T. Tam, Wendy Zhang, Tian Shen, Rolando Barrios, Julio Montaner, Kate Salters, Richard Lester, Surita Parashar, David Moore

Bibliographic record

VenueJMIR mhealth and uhealth · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversity of British ColumbiaAIDS Vancouver
Fundersnot available
KeywordsPrimary caremHealthIntervention (counseling)PhoneMobile phoneService (business)Short Message ServiceCohort studyCohort

Abstract

fetched live from OpenAlex

Background: Many individuals in urban low-income settings face barriers to engaging in primary care and experience systemic challenges such as homelessness and discrimination in the health care system. This study was conducted in the Downtown Eastside of Vancouver, Canada, a low-income neighborhood with intersecting structural vulnerabilities and disproportionate rates of substance use disorders. Advancements in mobile health expand options for facilitating communication between primary care providers and clients. Objective: We conducted a pilot project that provided primary care clients a mobile phone and access to WelTel, a mobile health tool that uses a 2-way texting approach and sends weekly automated check-in messages. Our study measured phone retention, defined as retaining a study-supplied device and being reachable on the study-supplied device over a 6-month period and explored the acceptability and feasibility of WelTel among a cohort of clients with complex health challenges. Methods: Stratified random sampling was used to recruit participants from a larger cohort study of primary care clinic clients in the Downtown Eastside. The sample was stratified to ensure equal participation based on gender and Indigenous and non-Indigenous participants. In this mixed methods research, participants completed 3 surveys over a 6-month period from November 2022 to May 2023. The surveys assessed phone retention and functionality as well as phone use, including use of the WelTel platform. Clients who had access to a functional mobile phone after the follow-up period were invited to complete an in-person interview. The semistructured interviews explored clients' experiences with WelTel, primary care, and engagement with technology. Results: We enrolled 49 participants (median age 48 y; 53% women and 49% Indigenous) and interviewed 16 participants. A total of 44 clients completed the 6-month survey, and of those clients, 26 (59%) had a functional phone. However, only 14 (29%) clients retained the mobile device supplied during the study and completed the 6-month survey. Phone retention or access to a nonstudy phone was lower among those who had used opioids (18% for both) in the past 3 months (P<.01), as well as those who reported not having access to a cellphone at enrollment (25% and 15%, respectively) compared to those who did have phone access at enrollment (P=.05). Both the surveys and semistructured interviews indicated that WelTel was generally well received; 25 (96%) of those with a functional phone reported that they liked receiving the weekly messages. During qualitative interviews, the WelTel intervention was reported to strengthen client-provider relationships and create pathways for receiving care. Conclusions: Overall, the pilot study found this intervention feasible and acceptable to clients; however, barriers to phone retention were an ongoing challenge. Expanded enrollment in the WelTel service will allow us to examine whether it also facilitates engagement in primary care among marginalized urban populations.

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)0.015
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.212
GPT teacher head0.582
Teacher spread0.371 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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