Medical Device Donation Practices in Canada: A Survey of Donor and Recipient Perspectives
Bibliographic record
Abstract
Background and Objective: Although developing countries have been receiving donations of medical equipment for many years, a number of studies have indicated that a high percentage of donated equipment is never put into use. [1,3,4] Many of the reasons for this can be traced back to inadequate donation practices on the part of donor organizations. The objective of this study was to gain an improved understanding of the practices and challenges associated with medical equipment donations by Canadian charitable organizations. Material and Methods: Forty-one organizations (registered and non-registered charities, non-governmental organizations (NGOs), non-profit organizations, medical clinics, and hospitals) completed an online survey, and 16 respondents were interviewed via telephone or in person. In addition, representatives from 28 hospitals in Ghana were interviewed in person to gain an understanding of the recipient experience. Results: We observed that for many Canadian donor organizations there is room for improvement in formalizing procedures, testing to verify equipment functionality before shipping, providing additional support for recipients in the form of manuals, spare parts and training, and long-term monitoring of donated items to measure effectiveness. For recipients, the most common challenges faced were lack of spare parts, and lack of operating or service manuals. Despite these challenges, all of the Ghanaian survey respondents said that donated medical equipment benefited their hospitals. Conclusion: We concluded that because of staffing limitations in smaller donor organizations, and in order to better meet the needs of recipients, it would be beneficial for Canadian organizations to communicate and collaborate with one another to share resources and expertise when planning donations overseas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".