Understanding the factors affecting the attraction and retention of health professionals in rural and remote areas: a mixed-method study in Niger
Bibliographic record
Abstract
BACKGROUND: The critical shortage of human resources in health is a critical public health problem affecting most low- and middle-income countries, particularly in sub-Saharan Africa. In addition to the shortage of health professionals, attracting and retaining them in rural areas is a challenge. The objective of the study was to understand the factors that influence the attraction and retention of health professionals working in rural areas in Niger. METHODS: A mixed-method study was conducted in Tillabery region, Niger. A conceptual framework was used that included five dimensions. Three data collection methods were employed: in-depth interviews, documentary analysis, and concept mapping. In-depth interviews were conducted with three main actor groups: policy-makers and Ministry of Health officials (n = 15), health professionals (n = 102), and local health managers (n = 46). Concept mapping was conducted with midwifery students (n = 29). Multidimensional scaling and cluster analysis were performed to analyse the data from the concept mapping method. A content analysis was conducted for the qualitative data. RESULTS: The results of the study showed that the local environment, which includes living conditions (no electricity, lack of availability of schools), social factors (isolation, national and local insecurity), working conditions (workload), the lack of financial compensation, and individual factors (marital status, gender), influences the attraction and retention of health professionals to work in rural areas. Human resources policies do not adequately take into account the factors influencing the retention of rural health professionals. CONCLUSION: Intersectoral policies are needed to improve living conditions and public services in rural areas. The government should also take into account the feminization of the medical profession and the social and cultural norms related to marital status and population mobility when formulating human resources management policies.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 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".