Implementation Leadership in the Point of Care Nursing Context: A Systematic Review Comparing Two Measurement Tools
Bibliographic record
Abstract
Introduction: Implementation leadership (IL) are effective point of care (POC) nursing leadership behaviors that facilitate contexts conducive to the successful implementation of evidence-based practices (EBPs). However, no systematic evaluation of IL tools validated for the nursing context existed. Aims: The purpose of this systematic review was to compare iterations of two IL measurement tools, the Implementation Leadership Scale (ILS) and the iLEAD, for application in a nursing context; and to critically appraise and summarize the methodological quality of studies assessing their psychometric properties. Methods: A comprehensive search was conducted in four databases. Two reviewers independently screened titles and abstracts, reviewed full-text articles, and performed extraction into data tables. Statisticians appraised the quality control aspects. Findings were narratively summarized. Results: A total of 247 records were included, where 10 for the ILS (including different versions) and one for the iLEAD met the inclusion criteria. Three studies evaluated the psychometric properties of the ILS in nursing, and its translations into Chinese and Greek. Content validity was deemed to be doubtful for both tools, but the ILS had adequate rating for comprehensiveness; methodological quality was very good for structural validity, internal consistency, hypothesis testing, and responsiveness where applicable for both scales, with the exception of cross-cultural validity which had ratings of adequate and inadequate for versions of the scales. Several study findings met the criteria for good measurement properties. No studies for either tool formally assessed feasibility. Conclusion: Applying validated and contextually relevant tools to evaluate the capacity of nursing leadership to engage in IL in real-world contexts are needed. The ILS shows promise but requires further validation for contexts with diverse and multiple nursing leaders at the POC. Feasibility needs to be further studied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".