Retention of medical doctors at the district level: a qualitative study of experiences from Tanzania
Bibliographic record
Abstract
BACKGROUND: Retention of Human Resources for Health (HRH), particularly doctors at district level is a big challenge facing the decentralized health systems in poorly resourced countries. Tanzania, with 75% of its population in rural areas, has only 26% of doctors serving in rural areas. We aimed to analyze the experiences regarding the retention of doctors at district level in Tanzania from doctors' and district health managers' perspectives. METHODS: A qualitative study was carried out in three districts from June to September 2013. We reviewed selected HRH documents and then conducted 15 key informant interviews with members of the District Health Management teams and medical doctors working at the district hospitals. In addition, we conducted three focus group discussions with Council Health Management Team members in the three districts. Incentive package plans, HRH establishment, and health sector development plans from the three districts were reviewed. Data analysis was performed using qualitative content analysis. RESULTS: None of the districts in this study has the number of doctors recommended. Retention of doctors in the districts faced the following challenges: unfavourable working conditions including poor working environment, lack of assurance of career progression, and a non-uniform financial incentive system across districts; unsupportive environment in the community, characterized by: difficulty in securing houses for rent, lack of opportunities to earn extra income, lack of appreciation from the community and poor social services. Health managers across districts endeavour to retain their doctors through different retention strategies, including: career development plans, minimum financial incentive packages and avenues for private practices in the district hospitals. However, managers face constrained financial resources, with many competing priorities at district level. CONCLUSIONS: Retention of doctors at district level faces numerous challenges. Assurance of career growth, provision of uniform minimum financial incentives and ensuring availability of good social services and economic opportunities within the community are among important retention strategies.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".