Survey of palliative care providers’ needs, perceived roles, and ethical concerns about addressing cancer family history at the end of life
Bibliographic record
Abstract
OBJECTIVE: Palliative care providers may face questions from patients and relatives regarding the heritability of cancers. Implications of such discussions for providers have been little explored. This study aimed to gather palliative care providers' views on their main needs, roles, and ethical concerns regarding cancer family history discussions. METHOD: The palliative care providers who participated in the 2015 and 2017 annual meetings of the Quebec Palliative Care Association were approached to complete a web-based questionnaire. Study participants answered the questionnaire between November 2016 and July 2017. They were asked to identify the most facilitating factor for cancer family history discussions, as well as their most important knowledge needs, potential role, and ethical concerns. Descriptive analyses were conducted. RESULTS: Ninety-four palliative care providers answered the questionnaire. Access to specialized resources to obtain information and protocols or guidelines were considered the most facilitating factors for cancer family history discussions by 32% and 20% of providers, respectively. Knowledge of hereditary cancers was the most relevant educational need for 53%. Thirty-eight per cent considered essential to be informed about their rights and duties regarding cancer family history discussions. Being attentive to patients' concerns and referring families to appropriate resources were identified as the most relevant roles for palliative care providers by 47% and 34% of respondents, respectively. Fifty-eight per cent agreed that cancer family history discussions should be initiated only if beneficial to family members. SIGNIFICANCE OF RESULTS: Education on hereditary cancers made consensus among palliative care providers as the most important knowledge need regarding discussing cancer family history at the end of life. Nonetheless, other less commonly expressed needs, including access to genetics specialists, protocols, or guidelines, and awareness of provider rights and duties concerning such discussions, deserve attention. Answering providers' needs might help optimize cancer predisposition management in palliative care.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".