A framework for the management of donated medical devices based on perspectives of frontline public health care staff in Ghana
Bibliographic record
Abstract
Background: Transnational funders provide up to 80% of funds for medical devices in resource-limited settings, yet sustained access to medical devices remains unachievable. The primary goal of this study was to identify what factors hinder access to medical devices through the perspectives of frontline public hospital staff in Ghana involved in the implementation of transnational funding initiatives. Methods: A case study was developed that involved an analysis of semi-structured interviews of 57 frontline technical, clinical and administrative public health care staff at 23 sites in Ghana between March and April 2017; a review of the national guidelines for donations; and images of abandoned medical devices. Results: Six key themes emerged, demonstrating how policy, collaboration, quality, lifetime operating costs, attitudes of health care workers and representational leadership influence access to medical devices. An in-depth assessment of these themes has led to the development of an enterprise-wide comprehensive acquisition and management framework for medical devices in the context of transnational funding initiatives. Conclusion: The findings in this study underscore the importance of incorporating frontline health care staff in developing solutions that are targeted at improving delivery of care. Sustained access to medical devices may be achieved in Ghana through the adoption of a rigorous and comprehensive approach to acquisition, management and technical leadership. Funders and public health policy makers may use the study's findings to inform policy reform and to ensure that the efforts of transnational funders truly help to facilitate sustainable access to medical devices in Ghana.
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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 | 0.033 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.044 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| 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".