Caring for Your Bariatric Patient: A Resource Guide to the Literature on Care of the Morbidly Obese
Bibliographic record
Abstract
In response to escalating obesity in Manitoba, Canada, the Winnipeg Regional Health Authority (WRHA) formed a Bariatric Care Committee to address the issue of providing safe, efficient, and effective care for this population. The WRHA proposed that Deer Lodge Centre (DLC) be designated as the site for the management and care of bariatric patients requiring long-term chronic care. As a result, a DLC Bariatric Committee with several working groups was formed. The working groups were to provide plans for clinical program, communication, research and education, staffing, capital planning, and equipment. The Research and Education Working Group conducted literature reviews for each of the working groups. A selection of the most pertinent resources found for the DLC Bariatric Committee Working Groups are highlighted here. It is anticipated that this resource will provide assistance for others wishing to establish bariatric programs within their facility. Citations are categorized under the headings: clinical care (activities of daily living, airway management, and skin care), comprehensive knowledge, staff education, equipment/capital planning, excellence, respect, geriatrics, and safe handling. This resource guide will be of interest for nurses caring for bariatric patients and for organizations providing long-term care of bariatric patients.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.023 |
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".