Bibliographic record
Abstract
Background The critical intersections of structural inequities and vulnerabilities of marginalized populations, particularly those engaging the social gradient of vulnerable groups or minority communities, are revealed in analysis of the Advance Care Planning within the Equity lens. Proposals for behavioural interventions in professional competencies including communication approaches for improving opportunities for ACP, focused largely on medical contexts often fail to address the root cause of the challenges of the ACP particularly as evidenced in misalignment of the ACP norms and protocol with values, trust and expectations of outcomes of population subgroups of the elderly, the homeless or members of diverse BIPOC (Black-Indigenous-Persons of Colour ) communities in pluralist western societies. Method The typical ACP approaches are analysed in terms of their key principles within the Equity lens to explore the complexities of the DEI (Diversity, Equity and Inclusion) dimensions of common misperceptions of the ACP (1) engaging the role of patients and (2) the misalignment of the ethical, legal and educational contexts of the ACP norms with quality care metrics. Results Approaches such as Partnering with Patients, and Communities provide valuable insights for ACP reform measures enhancing stakeholder knowledge about the medical protocol and expectations of the ACP. Yet the normative assumptions, seemingly oblivious of the disparate impact of the upstream factors of the Social determinants of health and illness, often fail to address the ‘whole person’ identities of diverse patients or intersections of multi-sector contexts of health system transformation. Conclusions The Poster suggests that within the Equity lens, acknowledgement of cultural variations in ACP as distinct frames of reference, present important opportunities for revisiting the traditional paradigm of the ACP and initiate sustainable transformative change.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.166 | 0.016 |
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".