Postings and transfers in the Ghanaian health system: a study of health workforce governance
Bibliographic record
Abstract
BACKGROUND: Decision-making on postings and transfers - that is, the geographic deployment of the health workforce - is a key element of health workforce governance. When poorly managed, postings and transfers result in maldistribution, absenteeism, and low morale. At stake is managing the balance between organisational (i.e., health system) and individual (i.e., staff preference) needs. The negotiation of this potential convergence or divergence of interests provides a window on practices of postings and transfers, and on the micro-practices of governance in health systems more generally. This article explores the policies and processes, and the interplay between formal and informal rules and norms which underpin postings and transfers practice in two rural districts in the Greater Accra Region of Ghana. METHODS: Semi-structured interviews were conducted with eight district managers and 87 frontline staff from the district health administration, district hospital, polyclinic, health centres and community outreach compounds across two districts. Interviews sought to understand how the postings and transfers process works in practice, factors in frontline staff and district manager decision-making, personal experiences in being posted, and study leave as a common strategy for obtaining transfers. RESULTS: Differential negotiation-spaces at regional and district level exist and inform postings and transfers in practice. This is in contrast to the formal cascaded rules set to govern decision-making authority for postings and transfers. Many frontline staff lack policy clarity of postings and transfers processes and thus 'test' the system through informal staff lobbying, compounding staff perception of the postings and transfers process as being unfair. District managers are also challenged with limited decision-space embedded in broader policy contexts of systemic hierarchy and resource dependence. This underscores the negotiation process as ongoing, rather than static. CONCLUSIONS: These findings point to tensions between individual and organisational goals. This article contributes to a burgeoning literature on postings and transfers as a distinct dynamic which bridges the interactions between health systems governance and health workforce development. Importantly, this article helps to expand the notion of health systems governance beyond 'good' governance towards understanding governance as a process of negotiation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".