Enacting Care by Being Experts and Managing Relationships: A Discourse Analysis of Medical Officer of Health Media Briefings
Bibliographic record
Abstract
Context In Canada, Medical Officers of Health (MOHs) 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. Objective To learn about MOH roles by analyzing their public communications at media briefings during the COVID-19 pandemic. Study Design and Analysis This study is a functional discourse analysis of transcribed MOH media briefings at three time points in six Canadian jurisdictions during the first full year of the COVID-19 pandemic (2020). Transcripts were coded and analyzed in an iterative, comparative process to understand the content, actions and purpose of MOH communication during media briefings. Setting or Dataset Twenty (20) Covid-19 media briefings conducted by Medical Officers of Health in Canada during 2020. Population Studied Medical Officers of Health Intervention/Instrument Media briefing transcripts Outcome Measures Role descriptions that emerged as themes from the discourse analysis Results MOHs 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. Conclusions The findings suggest that traditional medical roles were performed by MOHs 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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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".