Neuroimaging Methods for Nursing Science
Bibliographic record
Abstract
BACKGROUND: Since the inception of magnetic resonance imaging, thousands of studies have appeared in the literature reporting on multiple imaging techniques. However, there is a paucity of neuroimaging research programs developed by nurse scientists. OBJECTIVES: The purpose of this article is to introduce the nurse scientist to complex neuroimaging methods with the ultimate goal of creating impetus for future use of brain imaging in nursing research. METHODS: This article reviews common neuroimaging methods, presents vocabulary frequently used in neuroimaging work, provides information on access to resources in neuroimaging education, and discusses considerations for use of neuroimaging in research. RESULTS: Ten imaging modalities are reviewed, including structural and functional magnetic resonance imaging, computed tomography, positron emission tomography, and encephalography. DISCUSSION: Choosing an imaging modality for research depends on the nature of the research question, needs of the patient population of interest, and resources available to the novice and seasoned nurse scientist. Neuroimaging has the potential to innovate the study of symptom science and encourage interdisciplinary collaboration in research.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".