Online perspectives of deemed consent organ donation legislation in Nova Scotia: A thematic analysis of commentary in Facebook groups (Preprint)
Bibliographic record
Abstract
BACKGROUND The Canadian province of Nova Scotia recently became the first jurisdiction in North America to implement deemed consent organ donation legislation. Changing the consent models constituted one aspect of a larger provincial program to increase organ and tissue donation and transplantation rates. Deemed consent legislation can be controversial among the public, and public participation is integral to the successful implementation of the program. OBJECTIVE Social media constitutes key spaces where people express opinions and discuss topics, and social media discourse can influence public perceptions. This project examined how the public in Nova Scotia were responding to the legislative changes in Facebook groups. METHODS This project analyzed 2,337 comments on 26 relevant posts in 12 different public Nova Scotia-based Facebook groups. We conducted thematic and content analysis on the comments to determine how the public was responding to the legislative changes and how the participants interacted with one another in the discussions. RESULTS Our thematic analysis revealed principle themes which supported and critiqued the legislation, which raised specific issues, and which reflected on the topic from a neutral perspective. Subthemes showed individuals presenting perspectives through a variety of themes which included compassion, anger, frustration, mistrust, and a range of argumentative tactics. Comments included personal narratives, beliefs about government, altruism, autonomy, misinformation, and reflections on religion and death. Content analysis revealed that Facebook users react to popular comments with “likes” more so than other reactions. Comments with the most reactions included both negative and positive perspectives about the legislation. Personal donation and transplantation success stories as well as attempts to correct misinformation were some of the most “liked” positive comments. CONCLUSIONS The findings provide key insights into Nova Scotian perspectives on deemed consent legislation as well as organ donation and transplantation broadly. The insights derived from this analysis can contribute to public understanding, policy creation, and public outreach efforts that might take place in other jurisdictions considering the enactment of similar legislation. CLINICALTRIAL N/A
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".