The positive impact over time of Master’s level education on nurses’ utilization of nursing research-related tasks in clinical practice – A longitudinal cohort study
Bibliographic record
Abstract
Objective: To describe and compare the development of Master of Science in Nursing graduates’ utilization and improvement of nursing research-related tasks and knowledge in daily clinical practice, six months and twelve months after graduation.Methods: A longitudinal cohort study of 65 Master of Science in Nursing (MSN) graduates from a Danish university was conducted from 2016 to 2017. Data were collected six and twelve months after graduation using a purposive-constructed questionnaire based on four validated questionnaires. Data were analyzed using descriptive statistics and STATA software (12.0).Results: The overall results of the longitudinal cohort study showed a positive impact 12 months after graduation on the MSN graduates’ development and improvement of their utilization of nursing research-related tasks and knowledge in clinical practice. The results also showed a development in the MSN graduates’ employment in academic positions, as well as an increase in the number of hours per week spent on nursing research-related tasks.Conclusions: Providing nurses with Master’s level knowledge and skills can make a difference for them in clinical practice. However, knowledge is still needed on how the MSN qualification can have an impact on patient care: Future research must focus on practical observations of how the Msn graduates use their academic knowledge and skills to improve patient care, using specific outcomes and observable criteria.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".