Knowledge of journal impact factors among nursing faculty: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: The research assessed nursing faculty awareness and knowledge of the journal impact factor (JIF) and its impact on their publication choices. METHODS: A qualitative cross-sectional questionnaire was developed using Fluid Survey and distributed electronically to nursing faculty and instructors at three post-secondary institutions in Saskatchewan. Data were collected on place and status of employment, knowledge and awareness of JIFs, and criteria used to choose journals for publication. RESULTS: A total of forty-four nursing faculty and instructors completed the questionnaire. The authors found that faculty lack awareness or complete understanding of JIFs and that JIFs are not the most important or only criterion used when they choose a journal for publication. CONCLUSIONS: There are various reasons for choosing a journal for publication. It is important for librarians to understand faculty views of JIFs and their criteria for choosing journals for publication, so that librarians are better equipped to guide researchers in considering their academic goals, needs, and personal values.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".