Needs assessment of nurse researchers through a research lifecycle framework
Bibliographic record
Abstract
Objective: Health sciences librarian roles are evolving to better meet the needs of faculty. This study explores nursing faculty needs at the University of British Columbia through the research lifecycle framework of planning, conducting, disseminating, and assessing the impact of their research. Methods: A mixed methods survey study with Likert scale, multiple-choice, or ordinal ranking-scale questions and six open-response questions was conducted. The format was a web-based Qualtrics survey; participants had approximately three weeks to respond. Results: Nursing faculty identified the dissemination phase as benefiting most from library support prioritizing reference management and archiving research data as the top needs in that phase. Assessing impact skills such as citation analysis and Altmetrics training was ranked second. The Planning phase was ranked third with systematic review and literature review support most needed. The Conducting phase was identified as the phase where they needed the least support. Conclusion: Understanding the needs of researchers and enhancing scholar productivity is vital to offering responsive library research services. Across the research lifecycle, nursing faculty identified reference management, data management, metrics evaluation, systematic reviews, and literature reviews as the key areas for which they need support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.240 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".