Views of leaders in under-represented and equity-denied communities on organ and tissue donation in Nova Scotia, Canada, in light of the Human Organ and Tissue Donation Act: a qualitative descriptive study
Bibliographic record
Abstract
OBJECTIVE: To explore the views of underserved and equity-denied communities in Nova Scotia, Canada, regarding organ and tissue donation and deemed consent legislation. DESIGN: A qualitative descriptive study was undertaken, employing both interviews and focus groups. SETTING: The province of Nova Scotia, Canada-the first jurisdiction in North America to implement deemed consent legislation for organ and tissue donation. PARTICIPANTS: Leaders of African Nova Scotian, Lesbian, Gay, Bisexual, Trans, Queer, Two Spirit (LGBTQ2S+) and Faith-based communities (Islam and Judaism) were invited to participate (n=11). Leaders were defined as persons responsible for community organisations or in other leadership roles, and were purposively recruited by the research team. RESULTS: Through thematic analysis, four main themes were identified: (1) alignment with personal values as well as religious beliefs and perspectives; (2) trust and relationships, which need to be acknowledged and addressed in the context of deemed consent legislation; (3) cultural competence, which is essential to the roll-out of the new legislation and (4) communication and information to combat misconceptions and misinformation, facilitate informed decision-making, and mitigate conflict within families. CONCLUSIONS: Leaders of African Nova Scotian, LGBTQ2S+ and Faith-based communities in Nova Scotia are highly supportive of deemed consent legislation. Despite this, many issues exemplify the need for cultural competence at all levels. These findings should inform ongoing implementation of the legislation and other jurisdictions considering a deemed consent approach to organ and tissue donation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".