Exploring Danish clinical nurses’ experience with personalised medicine
Bibliographic record
Abstract
Personalised or precision medicine is expected to change healthcare significantly in the future. Growing attention is being devoted internationally as to how this development affects nursing care and demands educational initiatives for nurses. In Denmark, a lack of such educational initiatives seems evident. The aim of this study was to inquire into Danish nurses’ understanding of and experience with personalised medicine (PM) in their daily practice. Furthermore, the study comprised a search for courses about personalised medicine/precision medicine that foster educational inspiration. A questionnaire was distributed among Danish nurses. The respondents represented a wide spectrum of specialties. More than half of the respondents (52%) experienced daily or weekly that PM was part of the patient’s trajectory. More than a third (36%) encountered situations in which PM impacted patients’ treatment daily or weekly. More than four in every ten respondents reported that they collected family history data about diseases at least monthly. About two-thirds (66%) found education in PM to be relevant for nurses, and nearly half of the respondents (47%) indicated that they would find it very or somewhat relevant themselves to receive continuing PM education and training. We found only very few published papers describing educational PM interventions for nurses. In contrast, we found numerous online descriptions of PM courses for nurses. In conclusion, this study indicated that many Danish nurses encounter PM in their daily work. However, their expressed need for further knowledge on this subject cannot be accommodated through the current education offered.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".