Quality of care for chronic conditions: Identifying specificities of quality aims based on scoping review and Delphi survey
Bibliographic record
Abstract
ABSTRACT Background There is a need to implement good quality chronic care to address the ballooning burden of chronic conditions affecting all countries globally. However, to our knowledge, no systematic attempts have yet been made to define and specify aims for chronic care quality. Objective We conducted a scoping review and Delphi survey to establish and validate a comprehensive specification of chronic care quality aims. Methodology The Institute of Medicine’s (IOM) quality of care definition and aims was utilised as our base. We purposively selected scientific and grey literature that have acknowledged and unpacked the plurality of quality in chronic care and which proposed/made use of frameworks and studied their implementation or investigated minimum two IOM care quality aims and their implementation. We critically analysed the literature deductively and inductively. We validated our findings through Delphi survey involving international chronic care experts, mostly coming from/have expertise on low-and-middle-income countries. Results We considered the natural history of chronic conditions and the journey of a person with chronic condition to define and identify aims of chronic care quality. We noted that the six IOM aims apply but with additional meanings. We identified a seventh aim, continuity, which relates well to the issue of chronicity. Our panellists agreed with the specifications. Several provided contextualised interpretations and concrete examples. Conclusions Chronic conditions pose specific challenges underscoring the relevance of tailoring quality of care aims. Operationalization of this tailored definition and specified aims to improve, measure and assure quality of chronic care can be next steps.
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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 | 0.294 | 0.313 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".