The Nurse LEADership for Implementing Technologies – Mobile Health Model (Nurse LEAD-IT – mHealth)
Bibliographic record
Abstract
PURPOSE: Purpose of the paper: To describe the development of a testable conceptual model that examines the relationship between implementation leadership characteristics and nurses' use of mobile health technologies (mHealth) as part of their nursing practice. The model is currently being tested in the pan-Canadian context. CONCEPTS: Primary concepts and issues to be addressed: The 2014 and 2017 Canada-wide surveys of nurses' use of technologies reported seemingly low rates of mobile technology use: 3% in 2014 and 7% in 2017(Canada Health Infoway 2014, 2017). The agenda to increase the use of mHealth by nurses persists, though understanding of nurses' use of mHealth remains underexplored. Nurses' usage of mHealth has primarily been examined using technology acceptance models, which are limited in their ability to account for unique aspects of nursing practice. The Nurse LEADership for Implementing Technologies (Nurse LEAD-IT) - mHealth (Nurse LEAD-IT - mHealth) model draws concepts from implementation science, computer science, organizational behaviour, information science and nursing to develop a testable conceptual model. The model facilitates examination of the relationship between nurses' use of mHealth and leaders' implementation characteristics (proactivity, knowledge, support and perseverance) and technology acceptance (perceived usefulness and perceived ease of use), while controlling for factors that influence technology use (previous experience and voluntariness of use) and nurses' acceptance of evidence-based practice (age, gender and education). SIGNIFICANCE: Significance and/or implications for nurse leaders: The Nurse LEAD-IT model can aid in delineating the pragmatic skills and knowledge necessary for nursing leaders to successfully implement mHealth initiatives in nursing practice settings. Use of mHealth in Canadian nursing settings remain in the emergent stages; results from the testing of this conceptual model will be timely and important in informing current and future strategies by nursing leaders to best situate the development and implementation of mHealth for successful uptake and usage in nursing.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".