“Doctors ready to be posted are jobless on the street…” the deployment process and shortage of doctors in Tanzania
Bibliographic record
Abstract
BACKGROUND: The World Health Organization advocates that health workforce development is a continuum of three stages of entry, available workforce and exit. However, many studies have focused on addressing the shortage of numbers and the retention of doctors in rural and remote areas. The latter has left the contribution of the entry stage in particularly the deployment process on the shortage of health workforce less understood. This study therefore explored the experiences of medical doctors (MDs) on the deployment process after the internship period in Tanzania's health sector. METHODS: A qualitative case study that adopted chain referral sampling was used to conduct 20 key informant interviews with MDs who graduated between 2003 and 2009 from two Medical Universities in Tanzania between February and April 2016. These MDs were working in hospitals at different levels and Medical Universities in eight regions and five geo-political zones in the country. Information gathered was analysed using a qualitative content analysis approach. RESULTS: Experiences on the deployment process fall into three categories. First, "uncertainties around the first appointment" attributed to lack of effective strategies for identification of the pool of available MDs, indecision and limited vacancies for employment in the public sector and private sector and non-transparent and lengthy bureaucratic procedures in offering government employment. Second, "failure to respect individuals' preferences of work location" which were based on the influence of family ties, fear of the unknown rural environment among urbanized MDs and concern for career prospects. Third, "feelings of insecurity about being placed at a regional and district level" partly due to local government authorities being unprepared to receive and accommodate MDs and territorial protectionism among assistant medical officers. CONCLUSIONS: Experiences of MDs on the deployment process in Tanzania reveal many challenges that need to be addressed for the deployment to contribute better in availability of equitably distributed health workforce in the country. Short-term, mid-term and long-term strategies are needed to address these challenges. These strategies should focus on linking of the internship with the first appointment, work place preferences, defining and supporting career paths to health workers working under the local government authorities, improving the working relationships and team building at the work places and fostering rural attachment to medical students during medical training.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".