Bibliographic record
Abstract
AIM: The aim of this paper was to provide a concept analysis of 'pandemic influenza'. BACKGROUND: Pandemic influenza can have a devastating impact as individuals have little to no immunity towards the newly encountered virus. It is a persistent societal threat due to the advancement of multiple technological processes. Nurses work in multiple roles in pandemics. As such, a thorough understanding of the concept and its implications from a nursing perspective is required. DESIGN: Rodgers' Evolutionary Method was used to conduct the concept analysis of the term 'pandemic influenza'. DATA SOURCES: Forty-nine papers were examined from the disciplines of public health, medicine, law, bioethics and healthcare policy. Papers were found from the PubMed, CINAHL and Google Scholar databases all dates up to December 2013. Limits were set to include peer-reviewed, English language articles. METHODS: Identified papers were critically analyzed to explore the concept's antecedents, attributes and consequences. Surrogate and related terms, and an exemplar, were identified. RESULTS: Attributes of pandemic include original viral structure, increased human susceptibility, younger vulnerable populations and unpredictable time frames. Antecedents include processes that enable the increased geographical transmission of a newly created influenza. Consequences include higher morbidity and mortality rates and the need for an efficient pandemic response. CONCLUSIONS: This analysis identified the attributes of pandemic influenza through a synthesis of the current pandemic literature. However, no articles were identified as specifically nursing in nature. Therefore, more research is required to examine the impact of a pandemic declaration on the nursing profession.
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 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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".