Improving knowledge transfer on chronic respiratory diseases: A canadian perspective. How to translate recent advances in respiratory diseases into day-to-day care
Bibliographic record
Abstract
BACKGROUND: Chronic diseases represent an increasing burden for health care systems. Ongoing research efforts provide regularly new scientific evidence on how optimize current medical care. In regard to respiratory diseases, as for other health problems, optimal management of these conditions has been summarized in recent consensus guidelines but implementation of these recommendations is still poor. Not only are the key messages of such guidelines often unknown to the practitioner and the patient but even when it is, they are often insufficiently integrated into current care, often related to behavioral, organizational and communication barriers. METHODS: Literature review on the topic of Clinical Practice Guidelines implementation and reference to recent projects aimed at improving management of asthma in the province of Quebec and elsewhere, as models for such implementation process. RESULTS: The basic principles of an effective translation of current knowledge into day-to-day care are known, but healthcare delivery structures, practice tools and resources, and regional/local leadership should be available to make it happen. Ideally, implementation requires a multidisciplinary effort of care providers, specialists, general practitioners, allied health professionals, patients and their family. The general public, health administrators and policy makers should also be aware of the consequences of poor management of these diseases and be supportive of the proposed initiatives. Finally, these last should be adequately evaluated to ensure their effectiveness and determine if they should be improved. Recently projects performed in Quebec have proposed disease management models to identify asthma care gaps and improve translation of current Guidelines into day-to-day care. CONCLUSIONS: Although the human and socio-economical burden of chronic diseases is still increasing, their current management is still often deficient. In the recent decades, Practice Guidelines have been developed to guide Practitioners towards optimal care, but implementation of these Guides is still poor. Recent Canadian and International initiatives have proposed valid models to help address current care gaps.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".