Nurses’ leadership self-efficacy, motivation, and career aspirations
Bibliographic record
Abstract
Purpose This paper aims to test a model examining precursors and outcomes of nurses’ leadership self-efficacy, and their aspirations to management positions. Design/methodology/approach A cross-sectional survey of 727 registered nurses across Canada was conducted. Structural equation modelling using Mplus was used to analyse the data. Findings Results supported the hypothesized model: χ 2 (312) = 949.393; CFI = 0.927; TLI = 0.919; RMSEA = 0.053 (0.049-0.057); SRMR 0.044. Skill development opportunities ( ß = 0.20), temporary management roles ( ß = 0.12) and informal mentoring ( ß = 0.11) were significantly related to nurses’ leadership self-efficacy, which significantly influenced motivation to lead ( ß = 0.77) and leadership career aspirations ( ß = 0.23). Motivation to lead was significantly related to leadership career aspirations ( ß = 0.50). Practical implications Nurses’ leadership self-efficacy is an important determinant of their motivation and intention to pursue a leadership career. Results suggest that nurses’ leadership self-efficacy can be influenced by providing opportunities for leadership mastery experiences and mentorship support. Leadership succession planning should include strategies to enhance nurses’ leadership self-efficacy and increase front-line nurses’ interest in leadership roles. Originality value With an aging nurse leader workforce, it is important to understand factors influencing nurses’ leadership aspirations to develop and sustain nursing leadership capacity. This research study makes an important contribution to the nursing literature by showing that nurses’ leadership self-efficacy appears to be an important determinant of their motivation to lead and desire to pursue a career as a nurse leader.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".