Health personnel retention strategies in a peri-urban community: an exploratory study on Epworth, Zimbabwe
Bibliographic record
Abstract
BACKGROUND: The need to retain health personnel is a policy challenge undermining health system reform of the 21st century. The need to resolve this global health workforce crisis resulted in the First Global Forum on Human Resources for Health in 2008 from which the Kampala Declaration and Agenda for Global Action was formulated. However, whilst there have been several studies exploring the retention of health personnel towards this end, available literature does not provide a detailed narrative on strategies used in peri-urban communities. The aim of this study was to explore retention strategies implemented in a Zimbabwean peri-urban community between 2009 and 2014 and implications for peri-urban communities towards the health system reform agenda. METHODS: The study was carried out in Epworth, a peri-urban community in Harare, Zimbabwe. The research design was a cross-sectional survey, in which qualitative methods were used in sampling, data collection, reporting and analysis. Qualitative tools were used to collect data through in-depth interviews with purposively selected health personnel managers at 10 local clinics and sample interviews with purposively selected healthcare workers who included registered general nurses, state-certified nurses, midwives, environmental health technicians, nurse aids and community health volunteers at each clinic. Two focus group discussions were carried out with community health volunteers. Qualitative data was subjected to thematic analysis, with coding being performed manually. RESULTS: A programme-specific strategic partnership between the government and donor community contributed towards the mobilisation of more health personnel, health facilities, worker development and remuneration. To complement this, the Ministry of Health intervened through the review and payment of salaries, support towards post-basic training and development, and protection. The local board, mission and donors contributed through the payment of top-up allowances and provision of non-monetary incentives. CONCLUSIONS: The review of salaries, engagement of international strategic partners, payment of top-up allowances, support towards post-basic training and development, mobilisation of more health personnel, non-monetary incentives and healthcare worker protection were critical towards the retention of health personnel in the Epworth peri-urban community between 2009 and 2014.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".