Acceptability of active case finding with a seed-and-recruit model to improve tuberculosis case detection and linkage to treatment in Cambodia: A qualitative study
Bibliographic record
Abstract
BACKGROUND: With support of the national tuberculosis (TB) program, KHANA (a local non-governmental organization in Cambodia) has implemented an innovative approach using a seed-and-recruit model to actively find TB cases in the community. The model engaged community members including TB survivors as seed and newly diagnosed people with TB as recruiters to recruit presumptive TB cases in their social network in a snowball approach for screening and linkage to treatment. This study aimed to explore the acceptability of the active case finding with the seed-and-recruit model in detecting new TB cases and determine the characteristics of successful seeds. METHODS: This qualitative study was conducted in four provinces (Banteay Meanchey, Kampong Chhnang, Siem Reap, and Takeo) in Cambodia in 2017. Fifty-six in-depth interviews and ten focus group discussions (with a total of 64 participants) were conducted with selected beneficiaries and key stakeholders at different levels to gain insights into the acceptability, strengths, and challenges in implementing the model and the characteristics of successful seeds. Transcripts were coded and content analyses were performed. RESULTS: The seed-and-recruit active case finding model was generally well-received by the study participants. They saw the benefits of engaging TB survivors and utilizing their social network to find new TB cases in the community. The social embeddedness of the model within the local community was one of the major strengths. The success of the model also hinges on the integration with existing health facilities. Having an extensive social network, being motivated, and having good knowledge about TB were important characteristics of successful seeds. Study participants reported challenges in motivating the presumptive TB cases for screening, logistic capacities, and high workload during the implementation. However, there was a general consensus that the model ought to be expanded. CONCLUSIONS: These findings indicate that the seed-and-recruit model is well-accepted by the beneficiaries and key stakeholders. Further studies are needed to more comprehensively evaluate the impacts and cost-effectiveness of the model for future expansion in Cambodia as well as in other resource-limited settings.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".