Mapping provider and consumer voices using the AACTT framework: a focus group study of advance care planning
Bibliographic record
Abstract
BACKGROUND: The provision of healthcare is complex. When evidence-practice gaps are identified, interventions to improve practice across multi-level systems are required. These interventions often consist of multiple interacting components and behaviours. To effectively address these complexities, it is crucial to first identify the specific roles and actions required at each stage of the intervention. This approach enables a thorough examination of what is working well and what needs to be optimised. The action, actor, context, target, time (AACTT) framework provides a consistent approach to identifying key elements such as 'who' (actor) does 'what' (action), 'where' (context), 'to or with whom' (target) and 'when' (time). To our knowledge the AACTT has not yet been applied: 1) to specify complex interventions across patient journeys; and 2) to investigate consumer views, despite the importance of patient-centred care. AIM: Using advance care planning (ACP) as an exemplar complex healthcare process, we describe a method for using the AACTT framework to 1) map a complex model of care across a patient journey 2) capture the consumer perspective; and 3) operationalise these perspectives by comparing across groups and identifying alignments or misalignments. METHODS: Two groups were recruited (healthcare professionals and consumers). Informed by the AACTT framework, four focus groups discussed the process of ACP across existing care pathways. Maps visually representing the perspectives and preferences of healthcare professionals and consumers were co-created iteratively. Qualitative data was deductively coded to the AACTT framework and inductively coded to identify themes within domains. Maps were circulated for critical feedback and refined. RESULTS: Healthcare professional (n-13) and consumer perspectives (n = 11) highlighted what is 'currently occurring' in practice, what is 'not occurring', and what 'should be occurring' to align practice with consumer preferences of care. Comparing participant perspectives identified that most misalignment occurred within the actor, context, and time domains. Misalignment was found predominantly in actions 'occurring sometimes', with no converging perspectives reported for the context and time domains. CONCLUSION: This novel application of the AACTT framework systematically brings in the consumer voice in ways that may influence the delivery of care. This approach to specifying healthcare professional and consumer perspectives across a complex care pathway identifies barriers that are not found with traditional mapping methods or in current applications of the AACTT framework.
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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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".