Using Implementation Science to Promote the Use of the G8 Screening Tool in Geriatric Oncology
Bibliographic record
Abstract
OBJECTIVES: Evidence supports the integration of geriatric assessment in the care of older adults with cancer. The G8 screening tool is a validated instrument to target a geriatric assessment. Use of the G8 tool in clinical practice, however, is suboptimal. We systematically analyzed the barriers and facilitators to G8 tool use in oncology clinics and selected interventions tailored to the local context to enhance its uptake. DESIGN: This qualitative study used semistructured interviews and site observations. SETTING: St. Michael's Hospital, Toronto, Canada. PARTICIPANTS: Ten participants including G8 tool adopters and stakeholders at St. Michael's Hospital were interviewed. MEASUREMENTS: An interview guide based on the Theoretical Domains Framework (TDF) was developed to identify beliefs about G8 tool use. Barriers and facilitators to G8 tool use were mapped to the TDF domains and corresponding intervention functions from the Capability, Opportunity, Motivation, and Behavior model. Evidence-based implementation strategies were selected from two databases. RESULTS: Key TDF domains influencing G8 tool use behavior were social/professional role, goals, beliefs about consequences, and social influences. The behavior change domains were mapped to four mechanisms of change: persuasion (conduct local consensus discussions), modeling (identify and prepare a champion), education (distribute educational materials), and enablement (use materials to prepare patients to be active participants in understanding the evidence behind the G8 tool and answering questions accurately). CONCLUSION: This study identified barriers to G8 tool use. Local consensus discussions, identifying and preparing a champion, using educational materials, and preparing patients to be active participants may be implementation strategies to improve G8 tool use. J Am Geriatr Soc 67:898-904, 2019.
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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 | 0.152 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".