Building Research Capacity in Nursing Academia in 2020: Is the Road Less Perilous?
Bibliographic record
Abstract
BACKGROUND: Building research capacity in nursing academic units continues to be a challenge. There are a number of external contextual factors and internal factors that influence individual faculty as well as the collective to engage successfully in research. PURPOSE: The overall aim of this opinion article is to provide an overview of the current external and internal, processes and structures, relevant to capacity of nursing faculty to engage in research. METHODS: To inform the external context, we reviewed national research funding trends for nursing. To inform the internal context, we provided an exemplar of the internal processes and structures designed to support research capacity building within our academic unit. RESULTS: Canadian Institutes of Health Research funding trends for research grants led by nurse principal applicants increased between 2010 and 2013, followed by a steady decline. In 2017 to 2018, there were only 24 research grants led by nurse principal applicants. These external challenges coupled with the traditional internal barriers, such as the imbalance between teaching and research time, threaten research capacity for nursing academics. CONCLUSION: Organizational strategies to promote research capacity within academic nursing units are a necessary requirement to move forward.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".