Online training to improve evidence-based leadership competencies among nurse leaders in Finland and China: study protocols for two randomised feasibility trials
Bibliographic record
Abstract
INTRODUCTION: This study protocol describes two randomised feasibility trials that will evaluate the feasibility and preliminary effectiveness of an online training course to improve evidence-based leadership competences among nurse leaders working in hospitals in Finland and China. METHODS AND ANALYSIS: Two randomised, parallel-group studies will be conducted separately: one in Finland (n=140) and one in China (n=160). Nurse leaders who fulfil the eligibility criteria will be randomly allocated (1:1) to participate in either the online evidence-based leadership training or conventional online training (reading material only). The primary outcomes will be acceptance of the online course (logging into the platform) and adherence in the online course (returned course tasks and drop-out rate of the participants). The secondary outcomes will be acceptance of the study regarding recruitment, feasibility of the eligibility criteria and outcome measures and potential effectiveness of the online course on leadership skills, evidence-based knowledge, attitudes, practice, self-efficacy, self-esteem and intention to leave. In addition, the feedback will be asked after the course. ETHICS AND DISSEMINATION: Two separate trials have received ethical clearance from local ethics committees (12/2022 in Finland, E2021167 in China). Permission to conduct the study will be granted by hospital authorities. All participants will provide electronic informed consent before baseline data are collected. The trial results will be published locally, nationally and internationally in professional and peer-reviewed journals, and shared at national and international meetings and conferences. TRIAL REGISTRATION NUMBERS: NCT05244512; NCT05244499.
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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 | 0.119 | 0.120 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.079 | 0.014 |
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".