A framework for chronic care quality: results of a scoping review and Delphi survey
Bibliographic record
Abstract
Frameworks conceptualising the quality of care abound and vary; some concentrate on specific aspects such as safety, effectiveness, others all-encompassing. However, to our knowledge, tailoring to systematically arrive at a comprehensive care for chronic conditions quality (CCCQ) framework has never been done. We conducted a scoping review and Delphi survey to produce a CCCQ framework, comprehensively delineating aims, determinants and measurable attributes. With the assumption that specific groups (people with chronic conditions, care providers, financiers, policy-makers, etc.) view quality of care differently, we analysed 48 scientific and 26 grey literature deductively and inductively using the Institute of Medicine's quality of care framework as the foundation. We produced a zero-version of the quality of chronic care framework, detailing aims, healthcare system determinants, and measurement mechanisms. This was presented in a Delphi survey to 49 experts with diverse chronic care expertise/experience around the world. Consensus was obtained after the first round, with the panel providing suggestions and justifications to expand the agreed-upon components. Through this exercise, a comprehensive CCCQ framework encompassing the journey through healthcare of people with chronic conditions was developed. The framework specifies seven CCCQ 'aims' and identifies health system determinants which can be acted upon with 'organising principles' and measured through chronic care quality 'attributes' related to structures, processes and outcomes. Tailoring quality of care based on the nature of the diseases/conditions and considering different views can be done to ensure a comprehensive offer of healthcare services, and towards better outcomes that are acceptable to both the health system and people with chronic conditions (PwCC).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".