Une revue intégrative de l’identité populaire de l’infirmière durant la pandémie de la COVID-19
Bibliographic record
Abstract
Context : The current COVID-19 context has placed nurses at the heart of the pandemic, due to the critical role they play within the population. However, media and professional discourses are influencing the identity and clinical practice of nurses.Objective : Review the literature on the construction of the popular identity of nurses and their roles during the COVID-19 pandemic.Method : Several data sources were consulted : Eureka, Google News, Education Resources Information Center (ERIC), Sociological Abstracts, Cumulative Index to Nursing Information and Allied Health Literature (CINAHL), MEDLINE, and Social Sciences Abstracts. Manual searches of government and professional sites were also conducted.Results : Of a total of 281 papers indexed, 73 were retained. The literature analysis identified the following themes : 1) identity images of nurses during this pandemic and their professional roles ; 2) the sometimes paradoxical nature of media and political discourse ; and 3) the fact that this discourse seems to influence clinical nursing practice, which is being assigned new roles.Conclusion : This article raises awareness among decision-makers about the multiple roles of nurses and the public image of nurses during a pandemic, and takes a critical look at popular discourses related to nurses' identity and how this identity changes during a pandemic.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".