Informatics competencies for nurse leaders: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Globally, health information technologies are now being used by nurses in a variety of settings. However, nurse leaders often do not have the necessary strategic and tactical informatics competencies to adequately ensure their effective adoption and use. Although informatics competencies and competency frameworks have been identified and developed, to date there has not been review or consolidation of the work completed in this area. In order to address this gap, a scoping review is being conducted. The objectives of this scoping review are to: (1) identify informatics competencies of relevance to nurse leaders, (2) identify frameworks or theories that have been used to develop informatics competencies for nurse leaders, (3) identify instruments used to assess the informatics competencies of nurse leaders and (4) examine the psychometric properties of identified instruments. METHODS: Using the Arksey and O'Malley five-step framework, a literature review will be conducted using a scoping review methodology. The search will encompass academic and grey literature and include two primary databases and five secondary databases. Identified studies and documents will be independently screened for eligibility by two reviewers. Data from the studies and documents will be extracted and compiled into a chart. Qualitative data will be subject to a thematic analysis and descriptive statistics applied to the quantitative data. ETHICS AND DISSEMINATION: Ethical approval was not required for this study. Results will be used to inform a future study designed to validate an instrument used to evaluate informatics competencies for nurse leaders within a Canadian context.
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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 | 0.094 | 0.092 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.073 | 0.015 |
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".