E-learning education solutions for caregivers in long-term care (LTC) facilities: new possibilities.
Bibliographic record
Abstract
CONTEXT: Online learning (referred to as e-learning throughout this article) has proved to be a useful tool for delivering accessible and convenient education to busy clinical healthcare workers. The ABS Management Company specifically designed a program to provide nurses and caregivers with the necessary knowledge and skills to improve the quality of care and the quality of life for the geriatric population in long-term care (LTC) facilities. OBJECTIVES: The purpose of the "Online Solutions: Quality Education for Quality Care in Long-Term Care" program is to use new educational pedagogies and innovative ways to conceptualise and deliver healthcare education to meet the complex issues and concerns of caregivers in LTC facilities. METHODS: During the one-year period that data were collected for this study, 881 caregivers completed the eight (one-hour) modules in the program. Of these, 753 (85%) completed the optional assessment (both the pre and post tests) for one or more of the eight modules. Therefore, of the 881 employees who reviewed all eight modules (881x8 = 7048 modules), 1046 modules (15%) had both pre-post test data upon which to build the analysis. FINDINGS: Information from the evaluation revealed learner improvement in pre-post test scores in excess of 10%, suggesting an increase in new and relevant skills and knowledge related to abuse and neglect, elopement, infection control, nutrition and hydration, pressure ulcers, provision of basic care and restraints. Moreover, the data indicated a reduction in the use of restraints and occurrence of pressure ulcers, suggesting that learners applied new knowledge and skills in the workplace. Finally, staff turnover rates decreased more than 20% suggesting greater job satisfaction after participating in the program. CONCLUSIONS: The research findings point to an urgent and unmet need to provide more accessible just-in-time, just-for-you education programs for caregivers in LTC facilities to ensure quality and efficient services to residents and their families.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".