The development of an end-of-life guide for community-dwelling clients diagnosed with advanced cancer
Bibliographic record
Abstract
BACKGROUND: Advance care planning [ACP] is essential to providing quality end-of-life [EOL] care and promoting the wishes, values, and goals of patients diagnosed with advanced cancer. A lack of community-specific ACP resources, such as guidebooks or EOL resources, was identified as a need for patients diagnosed with advanced cancer through a literature review and by the Palliative Care Community Team [PCCT] in Northumberland County, Ontario. PURPOSE: The practicum project aimed to develop a community-specific ACP resource to assist patients whom the PCCT supports in ACP discussions. METHODS: Three methods were used to collect information for this project. Initially, a literature review was conducted to determine the benefits, impacts, and implications of ACP resources for patients. An environmental scan was then performed to determine the currently available resources. Finally, consultations were conducted with palliative care stakeholders to determine what the community would find necessary for inclusion in the resource. RESULTS: The literature review determined that ACP discussions positively impact quality of life, ensure EOL care is a priority, and that ACP is completed appropriately. The environmental scan showed that resources are available, but the information included was irrelevant to the patients the PCCT supports. Consultations with key palliative care stakeholders in the community were done to determine what information should be included in a resource guide for patients diagnosed with advanced cancer and supported by the PCCT. CONCLUSION: The need for a community-specific ACP resource was identified, and an ACP guide for patients was developed based on the community's needs. The developed guide will assist patients diagnosed with advanced cancer in talking about ACP, preparing their families for after their death, and assisting in maintaining quality of life.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".