Tailoring implementation for a cancer self-management support intervention for patients starting chemotherapy.
Bibliographic record
Abstract
287 Background: Multiple implementation strategies are described in the literature; however, there is limited consensus on how to best tailor implementation to organizational and clinician readiness. We undertook a mixed-methods evaluation to inform tailored implementation of self-management support (SMS) in ambulatory cancer care as the first phase of a pilot randomized trial of the intervention in patients starting chemotherapy. Methods: Validated surveys, focus groups and interviews were undertaken with key stakeholders (oncologists, nurses, allied health, and administrative leaders) in the lung, colorectal and lymphoma disease site groups at 3 regional cancer centres in Ontario, Canada. Median responses to individual survey questions were classified as an enabler, barrier or neutral based on predetermined cut-offs. Enablers and barriers were triangulated with qualitative data and mapped to the Consolidated Framework for Implementation Research domains. Implementation strategies to address barriers were identified using the Expert Recommendations for Implementing Change tool. Results: Survey respondents represented all stakeholder groups (n = 78; respondent rate = 50%). Minimal variation was noted across stakeholders and centres. Overall, respondents held positive beliefs about the value of SMS, were familiar with the principles of SMS and felt there was a tangible fit among the intervention, individual beliefs, and existing workflows. Suboptimal communication networks and access to information about the adoption of SMS, as well as a lack of organizational commitment to implementing the intervention were identified as key implementation barriers. Qualitative data reinforced quantitative findings, namely that stakeholders value SMS but were unsure if it would translate into reduced treatment toxicities. 46 implementation strategies were identified based on perceived barriers, of which 28 (61%) were common to all 3 centres. Conclusions: Stakeholders at cancer centres acknowledged that SMS is valuable, but potential barriers to integration of SMS into routine ambulatory practice exist. The impact of the tailored implementation plans will be evaluated as part of the trial.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".