A lack of allocated research time challenges the extent of the implementation of evidence-based practice - A three-year retrospective follow-up cohort study of Master of Science in Nursing graduates
Bibliographic record
Abstract
Objective: To describe and compare the development of Master of Science in Nursing graduates’ research utilization and improvement of knowledge, one to three years after graduation, and to describe their beliefs, abilities and implementation regarding evidence-based practice in the workplace.Methods: Sixty-five Master of Science in Nursing (MSN) graduates, associated with an academic cohort, were invited to participate in a three-year retrospective follow-up study. Forty-five MSN graduates replied, providing a response rate of 69.23%. A questionnaire of four areas, consisting of the customary cohort questionnaire combined with the EBP Belief scale and the EBP Implementation scale, was sent to the participants using SurveyMonkey®.Results: An overall increased development in MSN graduates’ research utilization and knowledge improvement in all parameters was found from one to three years after graduation, along with greater knowledge of, and a stronger belief in the value of, evidence-based practice. However, a strong decrease in allocated time for research was found, leading to a very limited implementation of evidence-based practice by the MSN graduates.Conclusions: The study ends with a question about whether time is still an issue – even for academic nurses, who are educated and employed to implement evidence-based practice. If the barriers to nursing research are not taken seriously by nursing management then the extent of implementation of evidence-based practice and the improvement of quality in patient care and trajectories will continue to be very limited.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".