Harnessing the Potential of Care Aides in Long-Term Care Settings
Bibliographic record
Abstract
Background: In Canada, health care aides are the largest frontline workforce in long-term care (LTC) as they provide the majority of direct care to residents. Care aides are central to resident quality of care and quality of life, although their skill sets have lagged behind resident acuity. Training and education programs that focus on developing transformational leadership skills of LTC staff are essential in building a healthier work environment and are often seen as the key to raising standards. Aim: To assess the learning needs of care aides working in a LTC facility with a goal of developing an inclusive leadership training program that fosters transformational leadership behaviours of staff. Methods: Leadership training was offered to care aides between March 2015 and September 2015. Data were collected through in-depth semi-structured interviews with eleven care aides during the period in which the program was offered to assess changes in their leadership skills. Data were audiotaped, transcribed and a content analysis was performed. Results: Thematic analysis revealed three areas that supported development: ‘reaction’, ‘learning’, and ‘behavioural’ criteria. Participants rated satisfaction with the leadership training program content and material. Care aides also reported improved communication and expressed increased understanding of transformational leadership skills after attending the training program. The program was associated with a positive work environment including extra effort from staff, work effectiveness, and satisfaction with leadership. Conclusion: Given that care aides provide most of the bedside care in LTC, their empowerment and education is key to providing high-quality care to residents. The findings support the implementation of an inclusive leadership approach to improve knowledge and development of transformational leadership skills among LTC staff. Relevance to Practice: The study represents an important step in the evaluation of care aides’ leadership development. In addition to the themes identified by the care aides, previous training and gaps in knowledge identified could inform the design of future training programs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| 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".