Safe Patient Handling Programs and Injury Prevention for Eastern Health in NL \n \n
Bibliographic record
Abstract
Patient handling tasks are a leading contributor to injuries among healthcare workers, who are more likely to suffer from workplace-related injuries than individuals who work in other sectors. The Newfoundland and Labrador Department of Health and Community Services has developed an Injury Prevention Program (IPP) for nursing staff employed in Long Term Care (LTC) to promote safe patient handling and to prevent injuries to the staff. The IPP consists of education and training, installation of lifting equipment, and the creation of several new positions for program coordination, policy development, education, and training (NL Department of Health and Community Services, 2011). \nThe Department of Research, Eastern Health, is evaluating the IPP to determine the impact and effectiveness of this program on nursing staff and residents in LTC (Eastern Health, 2012). Our partners in the Department of Research asked the Newfoundland and Labrador Centre for Applied Health Research to complete a scan of the peer-reviewed literature related to safe patient handling, with particular interest in the types of programs or interventions that may be associated with reduced musculoskeletal injuries among nursing staff. \n“Safe patient handling” programs often involve multiple interventions such as worker education programs, physical conditioning or exercise programs, disability management, organizational policies, and/or the use of mechanical lifts or other patient transfer equipment. The published literature in this area includes a number of special topics that are not relevant to the particular needs of our partners for this report, such as lifting bariatric patients or muscular/spinal motion analysis during lifting. Given the project parameters specified by the Department of Research, Eastern Health, we formulated a search strategy that would enable us to focus on outcomes identified in their proposal, namely those related to the intervention process and the intervention outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
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".