Oncology care providers' perceptions and anticipated barriers regarding the use of geriatric assessment in routine clinic practice: A mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: Geriatric assessment (GA) is currently not a standard of cancer care across Canada. In the Canadian province of Saskatchewan, there are no known formal geriatric teams in outpatient oncology settings. Therefore, it is not known whether, how, and to what extent GA is performed in oncology clinics, or what supports are needed to carry out a GA. The objective of this study was to explore Saskatchewan oncology care providers' knowledge, perceptions, and practices regarding GA, and their perceived barriers to implementing formal GA. MATERIALS AND METHODS: In this mixed-methods study, oncology physicians and nurses within the Saskatchewan Cancer Agency (SCA) were invited to participate in an anonymous survey and individual open-ended interview. Quantitative survey data were analyzed using descriptive statistics; free-text responses provided in the survey were summarized. Data from interviews were analyzed using thematic analysis. RESULTS: A total of 19 physicians and 30 clinic nurses participated in the survey (response rate: 24% [physicians] and 38.0% [nurses]). In terms of cancer treatment and management, the majority (74% of physicians and 62% of nurses) stated considerations for older adults are different than younger patients. More than half (53% of physicians and 58% of nurses) reported making treatment and management decisions primarily based on judgement versus validated tools. For physicians whose practices involve prescribing chemotherapy (16/19), 75% rarely or never use validated tools (e.g., CARG, CRASH) to assess risk of chemotoxicity for older patients. Lack of time and supporting staff and feeling unsure as to where to refer older patients for help or follow-up were the most commonly voiced anticipated barriers to implementing GA. Two physicians and six nurses (n = 8) participated in the open-ended interviews. Main themes included: (1) tension between knowing the importance of GA versus capacity and (2) buy-in. DISCUSSION: Our findings review barriers and opportunities for implementing GA in oncology care in Saskatchewan and provides foundational knowledge to inform efforts to promote personalized medicine and to optimize cancer care for older adults with cancer in this region.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".