A survey of clinical nurses’ research needs for research support from university faculty
Bibliographic record
Abstract
Supporting university faculty who are engaging in clinical research through continuing education can increase the effectiveness of education. This descriptive study aimed to describe the needs of nurses who are pursuing research and to clarify future activities to support their research. Data were collected using a questionnaire. Participants included 249 nurses currently working at seven different hospitals and who had conducted research supported by a university from FY2007 to FY2016. The questionnaire assessed the degree of difficulty of pursuing research and the need for support. In total, 177 nurses were included in the final analysis (valid response rate: 65.8%). The ethics review question had a median score of 3, indicating moderate difficulty. Both the methods and data analysis questions had median scores of 5 on the support need scale, indicating a high need for support, whereas the presentation question had a median score of 3 (moderate need for support). The literature review had the lowest percentage of participants who reported being satisfied with the support received, at 83.1%; however, 98.1% were satisfied with the support received for the ethics review. These results indicate that the following factors should be addressed to better support research among clinical nurses: teaching methods, reviewing the literature, selecting an appropriate research method, and analyzing the data. Our results also demonstrated that nurses were relatively dissatisfied with the methods of communication used in research. Nurses reported that future support should emphasize improving methods of support and effectively clarifying the needs of human resource development.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".