Nurses’ professional identity and information needs in the time of Covid-19: A latent cluster analysis
Bibliographic record
Abstract
The aims of this study are to find out, how nurses differ regarding their professional identity in times of Covid-19 and if nurses of different clusters of professional identity also differ regarding their satisfaction with their information needs. To get more insights in nurses work situation in Covid-19, we asked nurses about information sources they use to get information about Covid-19, by whom they feel supported, and if they feel fear working in times of Covid-19. We conducted a cross-sectional study. We used online questionnaires. 266 Nurses in Germany (N = 266) participated in our study. The study was conducted during Covid-19 pandemic. Our results show that most nurses use information about Covid-19 provided by their organisation. Most of them find social support through their colleagues. There are two clusters of nurses with significantly different professional identities; these two clusters show significant differences regarding their satisfaction with information needs. The results of the study are important for nursing organisations that should provide information for their nurses and by this can have influence on their coping strategies.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".