ENVISIONING AND DEVELOPING A SYSTEM TO MEET LAST STAGES OF LIFE CARE NEEDS OF PATIENTS AND FAMILIES
Bibliographic record
Abstract
A multiple-perspective qualitative study applying the Appreciative Inquiry (AI) framework was carried out with the goal of (re-)building the system of care for individuals in their last stages of life in Ontario, Canada. The “Discover” and “Dream” phases within the AI framework aimed at understanding what factors enable patients and their family caregivers to positively perceive and appropriately access available services and supports in the current system of care. 26 clinically frail elderly patients and/or their family caregivers were interviewed, and their lived experience and encounter with the system were documented and coded using grounded theory principles. Rich narratives revealed the needs of patients and caregivers, and the barriers and supports they faced while attempting to navigate Ontario’s system for care. They identified the following processes as potential platforms for positive changes: diagnosis, prognosis, assessment, access, resources, advocacy, and communication. Patients’/Caregivers’ narratives were presented to 11 expert stakeholders from different professional groupings – medical, social, legal and ethics, administration and policy – who were then interviewed as part of the “Design” and “Destiny” phases within the AI framework. Expert stakeholders considered patients/family caregivers’ lived experience in the broader context, and made recommendations on how to motivate and implement a path and vision for system change. Stakeholders commented on the need for professional training in communicating sensitive issues with patient/family, public education and awareness regarding hospice and palliative care, enhanced advocacy supports, an expanded model for applying palliative care principles, and the need for different professional sectors to work collaboratively towards these goals.
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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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".