MétaCan
Menu
Back to cohort
Record W4389339838 · doi:10.2196/48767

Recruitment Strategies in the Integration of Mobile Health Into Sickle Cell Disease Care to Increase Hydroxyurea Utilization Study (meSH): Multicenter Survey Study

2023· article· en· W4389339838 on OpenAlexvenueno aff
Chinonyelum Nwosu, Hamda Khan, Rita Masese, Judith M. Nocek, Siera Gollan, Taniya Varughese, Sarah Bourne, Cindy Clesca, Sara Jacobs, Ana A. Baumann, Lisa M. Klesges, Nirmish Shah, Jane S. Hankins, Matthew P. Smeltzer

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Institutes of HealthNational Heart, Lung, and Blood InstituteAustralian Government
KeywordsDiseaseMedicineIntensive care medicineHealth careInternal medicineEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Hydroxyurea is an evidence-based disease-modifying therapy for sickle cell disease (SCD) but is underutilized. The Integration of Mobile Health into Sickle Cell Disease Care to Increase Hydroxyurea Utilization (meSH) multicenter study leveraged mHealth to deliver targeted interventions to patients and providers. SCD studies often underenroll; and recruitment strategies in the SCD population are not widely studied. Unanticipated events can negatively impact enrollment, making it important to study strategies that ensure adequate study accrual. OBJECTIVE: The goal of this study was to evaluate enrollment barriers and the impact of modified recruitment strategies among patients and providers in the meSH study in response to a global emergency. METHODS: Recruitment was anticipated to last 2 months for providers and 6 months for patients. The recruitment strategies used with patients and providers, new recruitment strategies, and recruitment rates were captured and compared. To document recruitment adaptations and their reasons, study staff responsible for recruitment completed an open-ended 9-item questionnaire eliciting challenges to recruitment and strategies used. Themes were extrapolated using thematic content analysis. RESULTS: Total enrollment across the 7 sites included 89 providers and 293 patients. The study acceptance rate was 85.5% (382/447) for both patients and providers. The reasons patients declined participation were most frequently a lack of time and interest in research, while providers mostly declined because of self-perceived high levels of SCD expertise, believing they did not need the intervention. Initially, recruitment involved an in-person invitation to participate during clinic visits (patients), staff meetings (providers), or within the office (providers). We identified several important recruitment challenges, including (1) lack of interest in research, (2) lack of human resources, (3) unavailable physical space for recruitment activities, and (4) lack of documentation to verify eligibility. Adaptive strategies were crucial to alleviate enrollment disruptions due to the COVID-19 pandemic. These included remote approaching and consenting (eg, telehealth, email, and telephone) for patients and providers. Additionally, for patients, recruitment was enriched by simplification of enrollment procedures (eg, directly approaching patients without a referral from the provider) and a multitouch method (ie, warm introductions with flyers, texts, and patient portal messages). We found that patient recruitment rates were similar between in-person and adapted (virtual with multitouch) approaches (167/200, 83.5% and 126/143, 88.1%, respectively; P=.23). However, for providers, recruitment was significantly higher for in-person vs remote recruitment (48/50, 96% and 41/54, 76%, respectively, P<.001). CONCLUSIONS: We found that timely adaptation in recruitment strategies secured high recruitment rates using an assortment of enriched remote recruitment strategies. Flexibility in approach and reducing the burden of enrollment procedures for participants aided enrollment. It is important to continue identifying effective recruitment strategies in studies involving patients with SCD and their providers and the impact and navigation of recruitment challenges. TRIAL REGISTRATION: ClinicalTrials.Gov NCT03380351; https://clinicaltrials.gov/study/NCT03380351. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/16319.

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)0.027
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 score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.144
GPT teacher head0.479
Teacher spread0.335 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Formative ResearchSame topicHemoglobinopathies and Related DisordersFrench-language works237,207