Safe Patient Handling Education in Nepal: A Canadian Perspective in Creating and Conducting Training in a Developing Country
Bibliographic record
Abstract
BACKGROUND: Safe patient handling (SPH) and positioning is a worldwide health care issue in both developed and developing countries. It is important for all caregivers and patients but especially for palliative care and oncology patients. While there is a wide breadth of curriculum available in developed countries, little information is available about how to teach SPH and positioning in a developing country where resources are very limited. OBJECTIVE/METHODOLOGY: This article describes the process and recommendations used by a Canadian multidisciplinary health professional team in developing SPH curriculums for palliative care communities in Nepal. Educational materials were developed to teach SPH to a group of health care professionals at a palliative care conference in Bhaktapur, Nepal, in addition to a number of other settings within the Kathmandu Valley area. RESULTS: Current barriers toward future development of SPH in Nepal are defined and discussed. The SPH curriculums that have been created are provided and specific recommendations are outlined. There is a dearth of research to guide SPH education in developing countries. SPH change requires long-term support and curriculums that are culturally sensitive and contextually appropriate.
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 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| 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".