Assessing quality of life and quality of care for palliative patients in a rural setting using a mixed method approach
Bibliographic record
Abstract
This study explores how individuals living with a life limiting illness experienced and understood their quality of life and quality of care. Currently, few studies have explored the perspectives of palliative patients. The purpose of this study is to provide an in-depth analysis of the experiences of palliative patients and their understanding of quality of life (QoL) and quality of care (QoC) within the context of the Shuswap region of British Columbia (B.C.). This study highlights challenges and strengths of rural palliative care with the goal of informing future policies, programming and services in the rural Shuswap and more broadly. A mixed method approach was used for this study; participants were interviewed twice using a semi-structured interview, completing the McGill Quality of Life Tool prior to the second interview. Interpretive description and descriptive statistics were used for data analysis. The findings of the study indicate that there is a highly inter-connected relationship between the quality of life (QoL) and quality of care (QoC) for the Shuswap rural palliative patients. The findings also indicate that overall the palliative patients living in the rural Shuswap are pleased with their QoL and QoC; however, all participants expressed concerns regarding the coordination of their health care services. Many participants attended multiple health care appointments per week and would like to see their appointments more coordinated. In addition, all of the participants shared their strong desire to remain as independent as possible. Therefore, this study’s findings suggest that care needs to be more coordinated from a patient centred perspective which could result in a higher QoL for patients
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".