Be known, be available, be mutual: a qualitative ethical analysis of social values in rural palliative care
Bibliographic record
Abstract
BACKGROUND: Although attention to healthcare ethics in rural areas has increased, specific focus on rural palliative care is still largely under-studied and under-theorized. The purpose of this study was to gain a deeper understanding of the values informing good palliative care from rural individuals' perspectives. METHODS: We conducted a qualitative ethnographic study in four rural communities in Western Canada. Each community had a population of 10, 000 or less and was located at least a three hour travelling distance by car from a specialist palliative care treatment centre. Data were collected over a 2-year period and included 95 interviews, 51 days of field work and 74 hours of direct participant observation where the researchers accompanied rural healthcare providers. Data were analyzed inductively to identify the most prevalent thematic values, and then coded using NVivo. RESULTS: This study illuminated the core values of knowing and being known, being present and available, and community and mutuality that provide the foundation for ethically good rural palliative care. These values were congruent across the study communities and across the stakeholders involved in rural palliative care. Although these were highly prized values, each came with a corresponding ethical tension. Being known often resulted in a loss of privacy. Being available and present created a high degree of expectation and potential caregiver strain. The values of community and mutuality created entitlement issues, presenting daunting challenges for coordinated change. CONCLUSIONS: The values identified in this study offer the opportunity to better understand common ethical tensions that arise in rural healthcare and key differences between rural and urban palliative care. In particular, these values shed light on problematic health system and health policy changes. When initiatives violate deeply held values and hard won rural capacity to address the needs of their dying members is undermined, there are long lasting negative consequences. The social fabric of rural life is frayed. These findings offer one way to re-conceptualize healthcare decision making through consideration of critical values to support ethically good palliative care in rural settings.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".