Talking the same language on patient empowerment: Development and content validation of a taxonomy of self‐management interventions for chronic conditions
Bibliographic record
Abstract
CONTEXT: The literature on self-management interventions (SMIs) is growing exponentially, but it is characterized by heterogeneous reporting that limits comparability across studies and interventions. Building an SMI taxonomy is the first step towards creating a common language for stakeholders to drive research in this area and promote patient self-management and empowerment. OBJECTIVE: To develop and validate the content of a comprehensive taxonomy of SMIs for long-term conditions that will help identify key characteristics and facilitate design, reporting and comparisons of SMIs. METHODS: We employed a mixed-methods approach incorporating a literature review, an iterative consultation process and mapping of key domains, concepts and elements to develop an initial SMI taxonomy that was subsequently reviewed in a two-round online Delphi survey with a purposive sample of international experts. RESULTS: The final SMI taxonomy has 132 components classified into four domains: intervention characteristics, expected patient/caregiver self-management behaviours, outcomes for measuring SMIs and target population characteristics. The two-round Delphi exercise involving 27 international experts demonstrated overall high agreement with the proposed items, with a mean score (on a scale of 1-9) per component of 8.0 (range 6.1-8.8) in round 1 and 8.1 (range 7.0-8.9) in round 2. CONCLUSIONS: The SMI taxonomy contributes to building a common framework for the patient self-management field and can help implement and improve patient empowerment and facilitate comparative effectiveness research of SMIs. Patient or public contribution. Patients' representatives contributed as experts in the Delphi process and as partners of the consortium.
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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 | 0.079 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".