What can motivate Lady Health Workers in Pakistan to engage more actively in tuberculosis case-finding?
Bibliographic record
Abstract
BACKGROUND: Many interventions to motivate community health workers to perform better rely on financial incentives, even though it is not clear that monetary gain is the main motivational driver. In Pakistan, Lady Health Workers (LHW) are responsible for delivering community level primary healthcare, focusing on rural and urban slum populations. There is interest in introducing large-scale interventions to motivate LHW to be more actively involved in improving tuberculosis case-finding, which is low in Pakistan. METHODS: Our study investigated how to most effectively motivate LHW to engage more actively in tuberculosis case-finding. The study was embedded within a pilot intervention that provided financial and other incentives to LHW who refer the highest number of tuberculosis cases in three districts in Sindh province. We conducted semi-structured interviews with 20 LHW and 12 health programme managers and analysed these using a framework categorising internal and external sources of motivation. RESULTS: Internal drivers of motivation, such as religious rewards and social recognition, were salient in our study setting. While monetary gain was identified as a motivator by all interviewees, programme managers expressed concerns about financial sustainability, and LHW indicated that financial incentives were less important than other sources of motivation. LHW emphasised that they typically used financial incentives provided to cover patient transport costs to health facilities, and therefore financial incentives were usually not perceived as rewards for their performance. CONCLUSIONS: This study indicated that interventions in addition to, or instead of, financial incentives could be used to increase LHW engagement in tuberculosis case-finding. Our finding about the strong role of internal motivation (intrinsic, religious) in Pakistan suggests that developing context-specific strategies that tap into internal motivation could allow infectious disease control programmes to improve engagement of community health workers without being dependent on funding for financial incentives.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".