Safe Patient Handling of the Bariatric Patient: Sharing of Experiences and Practical Tips When Using Bariatric Algorithms
Bibliographic record
Abstract
A large percentage of bariatric patients admitted into healthcare facilities will need assistance with patient handling activities such as bathing, hygiene care of skin and wounds, repositioning in bed, assisting out of bed, and toileting. For the dependent patient, the exertion, awkward postures, and spinal loads associated with providing this care put the patient and healthcare workers (HCWs) at risk for injury. Challenges are evident during the provision of care in meeting the staffing needs for increased time associated, number of workers required to assist, special skill knowledge required, and equipment availability. In order to provide for these challenges, care of the bariatric patient handling needs should be a component of existing patient handling programs. There has been significant work completed in this area by Dr. Audrey Nelson and the VISN 8 Patient Safety Center of Inquiry of Department of Veterans Affairs. They offer online tools and resources that include a Bariatric Tool Kit. Within this kit are algorithms that are guidelines for the maneuvers associated with patient handling. The algorithm often has more than one recommended choice, and the HCW needs to decide which of the provided choices would be most suited to the situation. The authors share their knowledge and experience using the algorithms and discuss recommended choices and techniques, including their rationale, based on their experiences. Additional practical tips are also included to assist HCWs in managing the difficult patient handling tasks.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".