Camouflaging nursing research-related tasks in clinical practice–Experiences of newly-graduated masters of science in nursing
Bibliographic record
Abstract
Objective: To explore and describe how newly-graduated Masters of Science in Nursing experienced engaging in nursing research-related tasks in daily clinical practice.Methods: Fifteen nurses withholding a Masters of Science in Nursing degree were recruited from our longitudinal cohort study and interviewed six months after graduation in December 2016 (n = 10) and in December 2017 (n = 5), respectively. Data were analysed using Graneheim and Lundmann’s qualitative manifest and latent content analysis. Lincoln and Guba’s four criteria of trustworthiness were followed.Results: The main theme of the overall interpretation was Camouflaging nursing research-related tasks in clinical practice. The main theme describe the Master of Science in Nursing graduates as highly motivated to use their new academic skills in clinical practice and how they have to hide their engagement in research due to the barriers, which are outlined in the three themes: the position as time restrainer, the management as gatekeeper, and the nursing culture as norm setter.Conclusions: The study contributes with knowledge on how the Master of Science in Nursing graduates struggle to use their academic skills in clinical practice and how they felt the need to camouflage their commitment in research because it was not well reputed among their colleagues.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".