What about lay counselors’ experiences of task-shifting mental health interventions? Example from a family-based intervention in Kenya
Bibliographic record
Abstract
BACKGROUND: A key focus of health systems strengthening in low- and middle-income countries is increasing reach and access through task-shifting. As such models become more common, it is critical to understand the experiences of lay providers because they are on the forefront for delivering care services. A greater understanding would improve lay provider support and help them provide high-quality care. This is especially the case for those providing mental health services, as providing psychological care may pose unique stressors. We sought to understand experiences of lay counselors, focusing on identity, motivation, self-efficacy, stress, and burnout. The goal was to understand how taking on a new provider role influences their lives beyond simply assuming a new task, which would in turn help identify actionable steps to improve interventions with task-shifting components. METHODS: Semi-structured interviews (n = 20) and focus group discussions (n = 3) were conducted with three lay counselor groups with varying levels of experience delivering a community-based family therapy intervention in Eldoret, Kenya. Thematic analysis was conducted, including intercoder reliability checks. A Stress Map was created to visualize stress profiles using free-listing and pile-sorting data collected during interviews and focus group discussions. RESULTS: Counselors described high intrinsic motivation to become counselors and high self-efficacy after training. They reported positive experiences in the counselor role, with new skills improving their counseling and personal lives. As challenges arose, including client engagement difficulties and balancing many responsibilities, stress and burnout increased, dampening motivation and self-efficacy. In response, counselors described coping strategies, including seeking peer and supervisor support, that restored their motivation to persevere. At case completion, they again experienced high self-efficacy and a desire to continue. CONCLUSIONS: Findings informed suggestions for ways to incorporate support for lay providers into task-shifting interventions at initiation, during training, and throughout implementation. These include acknowledging and preparing counselors for challenges during training, increasing explicit attention to counselor stress in supervision, fostering peer support among lay providers, and ensuring a fair balance between workload and compensation. Improving and building an evidence base around practices for supporting lay providers will improve the effectiveness and sustainability of lay provider-delivered interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".