Human resource for health reform in peri-urban areas: a cross-sectional study of the impact of policy interventions on healthcare workers in Epworth, Zimbabwe
Bibliographic record
Abstract
BACKGROUND: The need to understand how healthcare worker reform policy interventions impact health personnel in peri-urban areas is important as it also contributes towards setting of priorities in pursuing the universal health coverage goal of health sector reform. This study explored the impact of post 2008 human resource for health reform policy interventions on healthcare workers in Epworth, a peri-urban community in Harare, Zimbabwe, and the implications towards health sector reform policy in peri-urban areas. METHODS: The study design was exploratory and cross-sectional and involved the use of qualitative and quantitative methods in data collection, presentation, and analysis. A qualitative study in which data were collected through a documentary search, five key informant interviews, seven in-depth interviews, and five focus group discussions was carried out first. This was followed by a quantitative study in which data were collected through a documentary search and 87 semi-structured sample interviews with healthcare workers. Qualitative data were analyzed thematically whilst descriptive statistics were used to examine quantitative data. All data were integrated during analysis to ensure comprehensive, reliable, and valid analysis of the dataset. RESULTS: Three main factors were identified to help interpret findings. The first main factor consisted policy result areas that impacted most successfully on healthcare workers. These included the deployment of community health workers with the highest correlation of 0.83. Policy result areas in the second main factor included financial incentives with a correlation of 0.79, training and development (0.77), deployment (0.77), and non-financial incentives (0.75). The third factor consisted policy result areas that had the lowest satisfaction amongst healthcare workers in Epworth. These included safety (0.72), equipment and tools of trade (0.72), health welfare (0.65), and salaries (0.55). CONCLUSIONS: The deployment of community health volunteers impacted healthcare workers most successfully. This was followed by salary top-up allowances, training, deployment, and non-financial incentives. However, health personnel were least satisfied with their salaries. This had negative implications towards health sector reform interventions in Epworth peri-urban community between 2009 and 2014.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".