Advance care planning for older people: The influence of ethnicity, religiosity, spirituality and health literacy
Bibliographic record
Abstract
In this discussion paper we consider the influence of ethnicity, religiosity, spirituality and health literacy on Advance Care Planning for older people. Older people from cultural and ethnic minorities have low access to palliative or end-of-life care and there is poor uptake of advance care planning by this group across a number of countries where advance care planning is promoted. For many, religiosity, spirituality and health literacy are significant factors that influence how they make end-of-life decisions. Health literacy issues have been identified as one of the main reasons for a communication gaps between physicians and their patients in discussing end-of-life care, where poor health literacy, particularly specific difficulty with written and oral communication often limits their understanding of clinical terms such as diagnoses and prognoses. This then contributes to health inequalities given it impacts on their ability to use their moral agency to make appropriate decisions about end-of-life care and complete their Advance Care Plans. Currently, strategies to promote advance care planning seem to overlook engagement with religious communities. Consequently, policy makers, nurses, medical professions, social workers and even educators continue to shape advance care planning programmes within the context of a medical model. The ethical principle of justice is a useful approach to responding to inequities and to promote older peoples' ability to enact moral agency in making such decisions.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".