Feasibility and usefulness of a leadership intervention to implement evidence-based falls prevention practices in residential care in Canada
Bibliographic record
Abstract
BACKGROUND: Leadership is critical to supporting and facilitating the implementation of evidence-based practices in health care. Yet, little is known about how to develop leadership capacity for this purpose. The aims of this study were to explore the (1) feasibility of delivering a leadership intervention to promote implementation, (2) usefulness of the leadership intervention, and (3) participants' engagement in leadership to implement evidence-based fall prevention practices in Canadian residential care. METHODS: = 10) included both formal (e.g., managers) and informal (e.g., nurses and care aids leaders). Outcome measures were parameters of feasibility (e.g., number of eligible candidates who attended the workshops and coaching sessions) and usefulness of the leadership intervention (e.g., ratings, suggested modifications). We conducted semi-structured interviews guided by the Implementation Leadership Scale (ILS), a validated measure of 12-item in four subcategories (proactive, supportive, knowledgeable, and perseverant), to explore the leadership behaviors that participants used to implement fall prevention practices. We repeated the ILS in a focus group meeting to understand the collective leadership behaviors used by the intervention team. Barriers and facilitators to leading implementation were also explored. RESULTS: = 10) attended the workshops and eight participated in at least one coaching session. Workshops and coaching were rated useful (≥ 3 on a 0-4 Likert scale where 4 = highly useful) by 71% and 86% of participants, respectively. Participants rated the O-MILe subcategories of supportive and perseverant leadership highest for individual leadership, whereas supportive and knowledgeable leadership were rated highest for team leadership. CONCLUSIONS: The leadership intervention was feasible to deliver, deemed useful by participants, and fostered engagement in implementation leadership activities. Study findings highlight the complexity of developing implementation leadership and modifications required to optimize impact. Future trials are now required to test the effectiveness of the leadership intervention on developing leadership for implementing evidence-based practices.
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 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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".