Making doctors stay: Rethinking doctor retention policy in a contracted-out primary healthcare setting in urban Bangladesh
Bibliographic record
Abstract
BACKGROUND: "Contracting Out" is a popular strategy to expand coverage and utilization of health services. Bangladesh began contracting out primary healthcare services to NGOs in urban areas through the Urban Primary Health Care Project (UPHCP) in 1998. Over the three phases of this project, retention of trained and skilled human resources, especially doctors, proved to be an intractable challenge. This paper highlights the issues influencing doctor's retention both in managerial as well as service provision level in the contracted-out setting. METHODOLOGY: In this qualitative study, 42 Key Informant Interviews were undertaken with individuals involved with UPHCP in various levels including relevant ministries, project personnel representing the City Corporations and municipalities, NGO managers and doctors. Verbatim transcripts were coded in ATLAS.ti and analyzed using the thematic analysis. Document review was done for data triangulation. RESULTS: The most cited problem was a low salary structure in contrast to public sector pay scale followed by a dearth of other financial incentives such as performance-based incentives, provident funds and gratuities. Lack of career ladder, for those in both managerial and service delivery roles, was also identified as a factor hindering staff retention. Other disincentives included inadequate opportunities for training to improve clinical skills, ineffective staffing arrangements, security issues during night shifts, abuse from community members in the context of critical patient management, and lack of job security after project completion. CONCLUSIONS: An adequate, efficient and dedicated health workforce is a pre-requisite for quality service provision and patient utilization of these services. Improved career development opportunities, the provision of salaries and incentives, and a safer working environment are necessary actions to retain and motivate those serving in managerial and service delivery positions in contracting out arrangements.
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".