Health Workers’ Educational Training and Staffing Concerning Medication Errors, Fall Injuries, and Complaints among Older Adults
Bibliographic record
Abstract
Several mandatory and voluntary further training programs in healthcare and long-term care sectors are available in Canada. However, the relation between further training of care workers and quality of patient care in hospitals, home care settings, and residential care facilities are unclear. This study investigates the association of further training of nurses, healthcare workers, and care assistants, as well as the health workers’ staffing levels with quality of life of older adults in Canada. Cross-sectional data, which included quality of life variables, such as medication errors, fall injuries, and complaints of older adults across healthcare and social care sectors, were drawn from the Canadian National Survey of the Work and Health of Nurses. The additional training of health workers has a positive association with quality of elder care by reducing incidence of fall injury and medication error and increasing resident satisfaction of patients. Staffing level among health workers is also positively associated with these quality of life variables. The findings of the study suggest that health worker staffing level and further professional training can improve quality of life of older adults. This study is original in that it examined a national representative sample from Canada and quality of life variables. Previous studies have not used such a survey thus far. Moreover, this study is unique because it connects professional development and further education to quality of life factors, such as incidence of fall injuries and medication errors, and resident satisfaction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".