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Record W3004970923 · doi:10.2196/15096

A Mobile Phone–Based Sexual and Reproductive Health Intervention for Female Sex Workers in Kenya: Development and Qualitative Study

2020· article· en· W3004970923 on OpenAlexvenueno aff
Frances H Ampt, Kelly L’Engle, Megan S. C. Lim, Kate F. Plourde, Emily Mangone, Collins Mudogo Mukanya, Peter Gichangi, Griffins Manguro, Margaret Hellard, Mark Stoové, Matthew Chersich, Walter Jaoko, Paul A. Agius, Marleen Temmerman, Winnie Wangari, Stanley Lüchters

Bibliographic record

VenueJMIR mhealth and uhealth · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilBurnet Institute
KeywordsReproductive healthIntervention (counseling)Psychological interventionSexual and reproductive health and rightsUnintended pregnancyHealth promotionBehavior changePsychologyIntervention mappingmHealthMedicineNursingPopulationPublic healthSocial psychologyEnvironmental healthReproductive rightsFamily planning

Abstract

fetched live from OpenAlex

BACKGROUND: Female sex workers (FSWs) have high rates of both unintended pregnancy and HIV, but few health promotion interventions address their contraceptive needs or other sexual and reproductive health and rights (SRHR) concerns. A broader approach integrates contraceptive promotion with HIV and sexually transmitted infection (STI) prevention and management, alcohol awareness, gender-based violence and rights, and health care utilization. The Women's Health Intervention using SMS for Preventing Pregnancy (WHISPER) mobile phone intervention uses a participatory development approach and behavior change theory to address these high-priority concerns of FSWs in Mombasa, Kenya. OBJECTIVE: This paper aimed to (1) describe the process of development of the WHISPER intervention, its theoretical framework, key content domains and strategies and (2) explore workshop participants' responses to the proposed intervention, particularly with regard to message content, behavior change constructs, and feasibility and acceptability. METHODS: The research team worked closely with FSWs in two phases of intervention development. First, we drafted content for three different types of messages based on a review of the literature and behavior change theories. Second, we piloted the intervention by conducting six workshops with 42 FSWs to test and refine message content and 12 interviews to assess the technical performance of the intervention. Workshop data were thematically analyzed using a mixed deductive and inductive approach. RESULTS: The intervention framework specified six SRHR domains that were viewed as highly relevant by FSWs. Reactions to intervention content revealed that social cognitive strategies to improve knowledge, outcome expectations, skills, and self-efficacy resonated well with workshop participants. Participants found the content empowering, and most said they would share the messages with others. The refined intervention was a 12-month SMS program consisting of informational and motivational messages, role model stories portraying behavior change among FSWs, and on-demand contraceptive information. CONCLUSIONS: Our results highlight the need for health promotion interventions that incorporate broader components of SRHR, not only HIV prevention. Using a theory-based, participatory approach, we developed a digital health intervention that reflects the complex reality of FSWs' lives and provides a feasible, acceptable approach for addressing SRHR concerns and needs. FSWs may benefit from health promotion interventions that provide relevant, actionable, and engaging content to support behavior change.

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)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.165
GPT teacher head0.524
Teacher spread0.360 · 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 designQualitative
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

Citations17
Published2020
Admission routes1
Has abstractyes

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