Developing team leadership to facilitate guideline utilization: planning and evaluating a 3-month intervention strategy
Bibliographic record
Abstract
BACKGROUND: Research describes leadership as important to guideline use. Yet interventions to develop current and future leaders for this purpose are not well understood. AIM: To describe the planning and evaluation of a leadership intervention to facilitate nurses' use of guideline recommendations for diabetic foot ulcers in home health care. METHOD: Planning the intervention involved a synthesis of theory and research (qualitative interviews and chart audits). One workshop and three follow-up teleconferences were delivered at two sites to nurse managers and clinical leaders (n=15) responsible for 180 staff nurses. Evaluation involved workshop surveys and interviews. RESULTS: Highest rated intervention components (four-point scale) were: identification of target indicators (mean 3.7), and development of a team leadership action plan (mean 3.5). Pre-workshop barriers assessment rated lowest (mean 2.9). Three months later participants indicated their leadership performance had changed as a result of the intervention, being more engaged with staff and clear about implementation goals. CONCLUSIONS AND IMPLICATIONS FOR NURSING MANAGEMENT: Creating a team leadership action plan to operationalize leadership behaviours can help in delivery of evidence-informed care. Access to clinical data and understanding team leadership knowledge and skills prior to formal training will assist nursing management in tailoring intervention strategies to identify needs and gaps.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".