Facilitating best practice in aged care: exploring influential factors through critical incident technique
Bibliographic record
Abstract
Aim. The focus of this study is on the perspective of facilitators of evidence-based aged care in long-term care (LTC) homes about the factors that influence the outcome of their efforts to encourage nursing staff use of best practice knowledge. Design. Critical incident technique was used to examine facilitators' experiences. Methods. Thirty-four participants submitted critical incident stories about their facilitation experiences through face-to-face interviews, telephone interviews, and/or a web-based written questionnaire. The resultant 123 stories were analysed using an inductive qualitative approach. Results. Factors at individual and contextual levels impacted the success of facilitators' work. The approaches and traits of facilitators as well as the emotionality and intellectual capacity of nursing staff were the individual factors of influence. On a contextual level, the inherent leadership, culture, and workload demands within LTC homes, as well as externally imposed standards were influential. Conclusions. Primary factors influencing the facilitation of best aged care in LTC homes appear to be largely relational in nature and intimately connected to the emotionality of those who work within these settings. Enhancing the interactional patterns amongst staff and leaders as well as promoting a positive emotional climate may be particularly effective in promoting better aged care nursing practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".