Examining the Influencing Factors in Implementing Leadership Development Strategies for Canadian Academic Nurses: A Pre-implementation Study
Bibliographic record
Abstract
As Canadian nursing schools face pressures to expand enrollment and accelerate learning pathways, the shortage of academic nurses threatens the sustainability of nursing education. With many faculty members nearing retirement, ongoing recruitment challenges, and an increasingly complex postsecondary environment, there is an urgent need to develop strong academic nursing leaders who can find innovative solutions to these challenges. This study examined the factors that influence the implementation of leadership development strategies for academic nurses. Using a pre-implementation lens guided by the Consolidated Framework for Implementation Research 2.0 and anchored in the LEADS in a Caring Environment Framework, it explored the contextual nuances influencing the uptake of leadership development strategies within Canadian nursing schools. Informed by the interpretivism-constructivism paradigm, this study used a qualitative descriptive approach.Findings revealed that implementing leadership development strategies is shaped by six influencing factors: 1) creating opportunities to expand networks, 2) nurturing a leadership ecosystem, 3) building capacity, 4) understanding academic processes and policies, 5) maintaining a strategic focus, and 6) managing resources. While these factors play a distinct role in shaping the uptake of leadership development strategies, they are interdependent, serving as either enablers or barriers. To synthesize these insights, this study distilled the six factors into three overarching messages: 1) the complex interplay of influencing factors, 2) institutional policies and external influences, and 3) strategic leadership. This synthesis allowed for a more cohesive perspective, helping to connect individual findings to broader system-level insights. By grouping the factors, the study provided a more accessible framework to guide academic leaders in understanding where to focus their efforts. It moves beyond isolated observations and toward integrated, actionable knowledge, offering pragmatic direction for those aiming to embed leadership development in meaningful and sustainable ways. Canadian nursing schools will be better positioned to advance their leadership capacity by recognizing and addressing the six influencing factors affecting the implementation of leadership development strategies. A plan that considers and accounts for these influencing factors will support creating a fulsome leadership development strategy capable of building a resilient pipeline of nursing leaders, ultimately strengthening both nursing education and the profession.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".