Systematic adaptation of public health palliative care interventions across settings using ADAPT guidance: Methodological learnings from the EU NAVIGATE project
Bibliographic record
Abstract
BACKGROUND: Systematic adaptation of evidence-informed interventions is critical for effective transfer across settings. Public health palliative care interventions pose unique challenges because of their complexity and embedding in dynamic, real-life settings. The ADAPT guidance provides a comprehensive framework for systematically adapting evidence-informed health interventions, yet its application in public health palliative care remains unexplored. AIM: Within the EU NAVIGATE project, this study describes the international adaptation process of a Canadian navigation program supporting older people with cancer experiencing declining health, for implementation in six European countries. It also reflects on the methodological insights gained from applying the ADAPT guidance in public health palliative care. DESIGN: Using an iterative five-stage multi-method approach, we followed the ADAPT guidance and its recommended frameworks. Stage 1 assessed context-intervention fit and identified core and adaptable components of the original intervention. Stage 2 adapted implementation materials, while stage 3 involved a contextual analysis. Stage 4 focused on adapting the training for implementers, and stage 5 reviewed feasibility. RESULTS: The ADAPT guidance proved flexible and useful, though systematic adaptation posed challenges due to the unique complexities of public health palliative care interventions. These included balancing intervention integrity with cultural sensitivities and local juridical regulations regarding end of life. Our process addressed these challenges through contextual assessments, identifying core components, engaging with original developers, and collaboration between local and international adaptation teams. CONCLUSIONS: A systematic adaptation process, guided by the ADAPT guidance is feasible, but transferring public health palliative care interventions requires careful methodological, contextual, and conceptual considerations.
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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 | 0.301 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| 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".