A Qualitative Content Analysis of Comments on Press Articles on Deemed Consent for Organ Donation in Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In 2019, two Canadian provinces became the first jurisdictions in North America to pass deemed consent legislation to increase deceased organ donation and transplantation rates. We sought to explore the perspectives of the deemed consent legislation for organ donation in Canada from the viewpoint of individuals commenting on press articles. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: In this qualitative descriptive study, we extracted public comments regarding deemed consent from online articles published by four major Canadian news outlets between January 2019 and July 2020. A total of 4357 comments were extracted from 35 eligible news articles. Comments were independently analyzed by two research team members using a conventional content analysis approach. RESULTS: Commenters' perceptions of the deemed consent legislation for organ donation in Canada predominantly fit within three organizational groups: perceived positive implications of the bills, perceived negative implications of the bills, and key considerations. Three themes emerged within each group that summarized perspectives of the proposed legislation. Themes regarding the perceived positive implications of the bills included majority rules, societal effect, and prioritizing donation. Themes regarding the perceived negative implications of the bills were a right to choose, the potential for abuse and errors, and a possible slippery slope. Improving government transparency and communication, clarifying questions and addressing concerns, and providing evidence for the bills were identified as key considerations. CONCLUSIONS: If deemed consent legislation is meant to increase organ donation and transplantation, addressing public concerns will be important to ensure successful implementation.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".