Enacting care by being experts and managing relationships: A discourse analysis of chief medical officer of health media briefings during the COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Canada, Chief Medical Officers of Health (CMOHs) are responsible for protecting and promoting the health of their respective populations, but few studies have examined this role and its connections with the practice of medicine. In Canada and elsewhere, CMOHs and other public health physicians have articulated their actions as caring for their populations as patients. In order to understand the components of enacted care, this study is a functional discourse analysis of transcribed CMOH media briefings at three time points in five Canadian jurisdictions during the first full year of the COVID-19 pandemic (2020). Transcripts were coded and analysed in an iterative, comparative process to understand the content, actions and purpose of CMOH communication during media briefings. CMOHs used their public communications to enact their care of populations by "being experts" and "managing relationships". "Being experts" involved describing disease characteristics, assessing risk and evidence, framing risk and evidence, and making judgments about intervention and exemption. "Managing relationships" involved self-regulating emotions, acknowledging the emotions of others, seeking adherence and collaboration, and setting expectations and boundaries. The findings suggest that traditional biomedical roles were performed by CMOHs in media briefings, implying the existence of a patient (or multiple patient-like relationships) and supporting further research into the processes by which public health physicians care for populations as patients.
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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 | 0.066 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| 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 it