Implementation science and stigma reduction interventions in low- and middle-income countries: a systematic review
Bibliographic record
Abstract
BACKGROUND: Interventions to alleviate stigma are demonstrating effectiveness across a range of conditions, though few move beyond the pilot phase, especially in low- and middle-income countries (LMICs). Implementation science offers tools to study complex interventions, understand barriers to implementation, and generate evidence of affordability, scalability, and sustainability. Such evidence could be used to convince policy-makers and donors to invest in implementation. However, the utility of implementation research depends on its rigor and replicability. Our objectives were to systematically review implementation studies of health-related stigma reduction interventions in LMICs and critically assess the reporting of implementation outcomes and intervention descriptions. METHODS: PubMed, CINAHL, PsycINFO, and EMBASE were searched for evaluations of stigma reduction interventions in LMICs reporting at least one implementation outcome. Study- and intervention-level characteristics were abstracted. The quality of reporting of implementation outcomes was assessed using a five-item rubric, and the comprehensiveness of intervention description and specification was assessed using the 12-item Template for Intervention Description and Replication (TIDieR). RESULTS: A total of 35 eligible studies published between 2003 and 2017 were identified; of these, 20 (57%) used qualitative methods, 32 (91%) were type 1 hybrid effectiveness-implementation studies, and 29 (83%) were evaluations of once-off or pilot implementations. No studies adopted a formal theoretical framework for implementation research. Acceptability (20, 57%) and feasibility (14, 40%) were the most frequently reported implementation outcomes. The quality of reporting of implementation outcomes was low. The 35 studies evaluated 29 different interventions, of which 18 (62%) were implemented across sub-Saharan Africa, 20 (69%) focused on stigma related to HIV/AIDS, and 28 (97%) used information or education to reduce stigma. Intervention specification and description was uneven. CONCLUSION: Implementation science could support the dissemination of stigma reduction interventions in LMICs, though usage to date has been limited. Theoretical frameworks and validated measures have not been used, key implementation outcomes like cost and sustainability have rarely been assessed, and intervention processes have not been presented in detail. Adapted frameworks, new measures, and increased LMIC-based implementation research capacity could promote the rigor of future stigma implementation research, helping the field deliver on the promise of stigma reduction interventions worldwide.
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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 | 0.054 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".