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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".