Accommodate or Reject: The Role of Local Communities in the Retention of Health Workers in Rural Tanzania
Bibliographic record
Abstract
BACKGROUND: While over 70% of the population in Tanzania reside in rural areas, only 25% of physicians and 55% of nurses serve these areas. Tanzania operates a decentralised health system which aims to bring health services closer to its people through collaborative citizen efforts. While community engagement was intended as a mechanism to support the retention of the health workforce in rural areas, the reality on the ground does not always match this ideal. This study explored the role local communities in the retention of health workers in rural Tanzania. METHODS: An exploratory qualitative study was completed in two rural districts from the Kilimanjaro and Lindi regions in Tanzania between August 2015 and September 2016. Nineteen key informant interviews (KIIs) were conducted with district health managers, local government leaders, and health facility in-charges. In addition, three focus group discussions (FGDs) were conducted with 19 members of the governing committees of three health facilities from the two districts. Data were analysed using the thematic analysis technique. RESULTS: Accommodation or rejection were the two major ways in which local communities influenced the quest for retaining health workers. Communities accommodated incoming health workers by providing them a good reception, assuming responsibility for resolving challenges facing health facilities and health workers, linking health workers to local communities and promoting practices that placed a high value on health workers. On the flip side, communities could also reject health workers by openly expressing lack of trust and labelling them as 'foreigners,' by practicing cultural rituals that health workers feared and discrimination based on cultural differences. CONCLUSION: Fostering good relationships between local communities and health workers may be as important as incentives and other health system strategies for the retention of health workers in rural areas. The role communities play in rural health worker retention is not sufficiently recognized and is worthy of further research.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".