Building Research Capacity Among Community College Nursing Faculty
Bibliographic record
Abstract
BackgroundNursing faculty in colleges who teach in undergraduate programs in Canada typically partner with universities in collaborative relationships, to ensure students receive an undergraduate degree, which is essential for entry-to-practice. These collaborative programs have led to increased pressure for nursing faculty in colleges to engage in research as colleges are being held to the same accreditation standards as universities, with their emphasis on scholarship and research. College faculty face numerous barriers to engaging in research, and there has been little study of how research capacity is fostered among college facultyMethodsIn fall 2018, a participatory action research approach was taken to build research capacity within a group of 13 nursing faculty members in a college’s nursing program. Members met between October 2018 and March 2020, and thematic analysis was used to examine notes from the meetings.ResultsThree themes were identified: 1) encountering challenges; 2) leveraging strengths, and 3) building research expertise. Group members initiated four research projects which secured internal funding, and were initiated in fall 2019, and are now in the stage of analysis.DiscussionThis project has helped to foster a culture of research within this nursing program. The group is now transitioning to a community of practice for nursing faculty at the college focused on 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 | 0.055 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".