A systematic review of participatory approaches to empower health workers in low- and middle-income countries, highlighting Health Workers for Change
Bibliographic record
Abstract
This systematic review assesses participatory approaches to motivating positive change among health workers in low- and middle-income countries (LMICs). The mistreatment of clients at health centres has been extensively documented, causing stress among clients, health complications and even avoidance of health centres altogether. Health workers, too, face challenges, including medicine shortages, task shifting, inadequate training and a lack of managerial support. Solutions are urgently needed to realise global commitments to quality primary healthcare, country ownership and universal health coverage. This review searched 1243 titles and abstracts, of which 32 were extracted for full text review using a published critical assessment tool. Eight papers were retained for final review, all using a single methodology, 'Health Workers for Change' (HWFC). The intervention was adapted to diverse geographical and health settings. Nine indicators from the included studies were assessed, eliciting many common findings and documenting an overall positive impact of the HWFC approach. Health workers acknowledged their negative behaviour towards clients, often as a way of coping with their own unmet needs. In most settings they developed action plans to address these issues. Recommendations are made on mainstreaming HWFC into health systems in LMICs and its potential application to alleviating stress and burnout from COVID-19.
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 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 | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".