[Development of quality of care indicators to support chronic disease management].
Bibliographic record
Abstract
INTRODUCTION: This article presents the results of a project conducted by the Institut national d'excellence en santé et en services sociaux of Québec to develop quality of care indicators for the management of six chronic illnesses. METHODS: Indicators were identified through literature searches and analysis of clinical practice guidelines (CPGs). Interdisciplinary expert panels assessed their validity and the strength of the evidence on which they were based. Representatives of patients (N = 19) and professionals (N = 29) were consulted on their relevance and acceptability. Indicators were categorized according to the Chronic Care Model (CCM). RESULTS: A total of 164 indicators were developed, 126 specific to the illnesses under study and 38 on processes and outcomes generic to the CCM. There was convergence between patients and professionals on the relevance of a majority of indicators. Professionals expressed concerns on the indicators measured by means of patient surveys that they considered to be too subjective. DISCUSSION: The importance given to CPGs as the main source of indicators resulted in a great number of indicators of the technical quality ofcare. Using the CCM contributed to a broader perspective of quality. The consultation process identified some of the concerns of professionals about indicator measurement, thusguidingfuture implementation initiatives.
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 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 | 0.036 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".