Bibliographic record
Abstract
The concept of whistleblowing, which began to emerge in the 1970s, has gained significant traction over time and across disciplines, including law, management, public administration, sociology, psychology, and health sciences. Interestingly, nurses and nursing students account for the majority of the participants in studies pertaining to whistleblowing. Nursing research conducted in the past two decades provide a good foundation on which to build a better understanding of the context in which whistleblowing takes place, the process of whistleblowing itself, and the repercussions experienced by whistleblowers, but major conceptual gaps remain. In fact, limited attention has been given to the conceptual underpinnings and the use of the concept of whistleblowing in nursing. The goal of the present conceptual analysis was to start addressing this gap and raise some critical questions about the future application of this concept in nursing, including potential opportunities and limitations. Our analysis allowed us to identify a number of antecedents, attributes, and consequences of whistleblowing in nursing. It also revealed three areas needing more attention: the concept itself, organizational culture, and research into the complexities of whistleblowing.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".