Media Discourse of Community Support for Frontline Healthcare Workers During the First Month of the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: The mental health of frontline healthcare workers (HCWs) can be challenged and strained during an infectious disease outbreak. Community support, as experienced in past outbreaks, may be helpful in mitigating these psychosocial effects. Examining the current unfolding of the coronavirus disease (COVID-19) pandemic for new insights, this study sought to understand how community support is represented and perceived as valuable to HCWs in written press media. Methods: Utilizing a media discourse methodology, written news media from the first month of the COVID-19 pandemic was sought for analysis. Included news media consisted of articles discussing community support for HCWs in London, Ontario. Results: A total of forty news articles pertaining to community support for HCWs was collected and analyzed. Forms of community support depicted by press media fell into the categories of acts of solidarity, discounted goods, donations of personal protective equipment, and personal services. The underpinning perceptions of news articles was community support for HCWs had a positive influence on HCWs well-being. Direct discussions with HCWs or representatives of HCW’s in press media only further reinforced this discourse of benefice. Conclusion: This analysis shows that community support for HCWs has been present since the beginning of the COVID-19 pandemic. As this crisis progresses, additional research should be conducted to monitor this discourse media for change in representation of and reception by HCWs.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".