Understanding Lay Counselor Perspectives on Mobile Phone Supervision in Kenya: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Task shifting is an effective model for increasing access to mental health treatment via lay counselors with less specialized training that deliver care under supervision. Mobile phones may present a low-technology opportunity to replace or decrease reliance on in-person supervision in task shifting, but important technical and contextual limitations must be examined and considered. OBJECTIVE: Guided by human-centered design methods, we aimed to understand how mobile phones are currently used when supervising lay counselors, determine the acceptability and feasibility of mobile phone supervision, and generate solutions to improve mobile phone supervision. METHODS: Participants were recruited from a large hybrid effectiveness implementation study in western Kenya wherein teachers and community health volunteers were trained to provide trauma-focused cognitive behavioral therapy. Lay counselors (n=24) and supervisors (n=3) participated in semistructured interviews in the language of the participants' choosing (ie, English or Kiswahili). Lay counselor participants were stratified by supervisor-rated frequency of mobile phone use such that interviews included high-frequency, average-frequency, and low-frequency phone users in equal parts. Supervisors rated lay counselors on frequency of phone contact (ie, calls and SMS text messages) relative to their peers. The interviews were transcribed, translated when needed, and analyzed using thematic analysis. RESULTS: Participants described a range of mobile phone uses, including providing clinical updates, scheduling and coordinating supervision and clinical groups, and supporting research procedures. Participants liked how mobile phones decreased burden, facilitated access to clinical and personal support, and enabled greater independence of lay counselors. Participants disliked how mobile phones limited information transmission and relationship building between supervisors and lay counselors. Mobile phone supervision was facilitated by access to working smartphones, ease and convenience of mobile phone supervision, mobile phone literacy, and positive supervisor-counselor relationships. Limited resources, technical difficulties, communication challenges, and limitations on which activities can be effectively performed via mobile phone were barriers to mobile phone supervision. Lay counselors and supervisors generated 27 distinct solutions to increase the acceptability and feasibility of mobile phone supervision. Strategies ranged in terms of the resources required and included providing phones and airtime to support supervision, identifying quiet and private places to hold mobile phone supervision, and delineating processes for requesting in-person support. CONCLUSIONS: Lay counselors and supervisors use mobile phones in a variety of ways; however, there are distinct challenges to their use that must be addressed to optimize acceptability, feasibility, and usability. Researchers should consider limitations to implementing digital health tools and design solutions alongside end users to optimize the use of these tools. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s43058-020-00102-9.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".