Media framing of emergency departments: a call to action for nurses and other health care providers
Bibliographic record
Abstract
Background As part of a larger study focused on interventions to enhance the capacity of nurses and other health care workers to provide equity-oriented care in emergency departments (EDs), we conducted an analysis of news media related to three EDs. The purpose of the analysis was to examine how media writers frame issues pertaining to nursing, as well as the health and social inequities that drive emergency department contexts, while considering what implications these portrayals hold for nursing practice. Methods We conducted a search of media articles specific to three EDs in Canada, published between January 1, 2018 and May 1, 2019. Media items (N = 368) were coded by story and theme attributes. A thematic analysis was completed to understand how writers in public media present issues pertaining to nursing practice within the ED context. Results Two overarching themes were found. First, in ED-related media that portrays health care needs of people experiencing health and social inequities, messaging frequently perpetuates stigmatizing discourses. Second, media writers portray pressures experienced by nurses working in the ED in a way that evades structural determinants of quality of care. Underlying both themes is an absence of perspectives and authorship from practicing nurses themselves. Conclusions We recommend that frontline nurses be prioritized as experts in public media communications. Nurses must be supported to gain critical media skills to contribute to media, to destigmatize the health care needs of people experiencing inequity who attend their practice, and to shed light on the structural causes of pressures experienced by nurses working within emergency department settings.
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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 | 0.095 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.031 | 0.039 |
| Scholarly communication | 0.039 | 0.052 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 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".