Evaluation of recruitment and retention strategies for health workers in rural Zambia
Bibliographic record
Abstract
BACKGROUND: In response to Zambia's critical human resources for health challenges, a number of strategies have been implemented to recruit and retain health workers in rural and remote areas. Prior to this study, the effectiveness of these strategies had not been investigated. The purpose of this study was to determine the impacts of the various health worker retention strategies on health workers in two rural districts of Zambia. METHODS: Using a modified outcome mapping approach, cross-sectional qualitative and quantitative data were collected from health workers and other stakeholders through focus group discussions and individual interview questionnaires and were supplemented by administrative data. Key themes emerging from qualitative data were identified from transcripts using thematic analysis. Quantitative data were analyzed descriptively as well as by regression modelling. In the latter, the degree to which variation in health workers' self-reported job satisfaction, likelihood of leaving, and frequency of considering leaving, were modelled as functions of participation in each of several retention strategies while controlling for age, gender, profession, and district. RESULTS: Nineteen health worker recruitment and retention strategies were identified and 45 health care workers interviewed in the two districts; participation in each strategy varied from 0% to 80% of study participants. Although a salary top-up for health workers in rural areas was identified as the most effective incentive, almost none of the recruitment and retention strategies were significant predictors of health workers' job satisfaction, likelihood of leaving, or frequency of considering leaving, which were in large part explained by individual characteristics such as age, gender, and profession. These quantitative findings were consistent with the qualitative data, which indicated that existing strategies fail to address major problems identified by health workers in these districts, such as poor living and working conditions. CONCLUSIONS: Although somewhat limited by a small sample size and the cross-sectional nature of the primary data available, the results nonetheless show that the many health worker recruitment and retention strategies implemented in rural Zambia appear to have little or no impact on keeping health workers in rural areas, and highlight key issues for future recruitment and retention efforts.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".